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As the relentless tide of technological advancement swells, the insurance industry, among many others, is facing a pivotal transformation. The inception and evolution of insurance technology or "insurtech" mandates that insurance agents, brokers, and companies diligently adapt and assimilate novel tools and methodologies to augment their operational efficiency and competitiveness. Of the emerging technologies, the innovative language model ChatGPT, conceived and developed by OpenAI, is showing significant potential to redefine the landscape of the insurance industry.This powerful AI model offers a diverse suite of services, each capable of improving the lives of insurance agents in numerous ways. From perfecting customer service and streamlining underwriting to improving data analytics and fraud detection, ChatGPT opens a Pandora's box of possibilities. Yet, the efficacy and feasibility of these innovative solutions call for a judicious understanding of the technology's strengths and limitations.The advantages of AI in InsuranceFirstly, customer service, the linchpin of the insurance business, is an area that stands to gain substantially from the implementation of ChatGPT. Insurance products and processes, notoriously labyrinthine to the average consumer, are sources of frequent queries and uncertainties. By employing ChatGPT, insurance firms can automatically answer routine questions related to policy details, billing, claims statuses, and more, in an array of languages. In doing so, it significantly alleviates the burden on customer service agents and concurrently boosts customer engagement.Such automated systems also find favor among modern consumers, with reports suggesting a notable preference for chatbot interactions. ChatGPT, with its impressive capabilities in generating human-like text responses, can amplify the effectiveness of customer service chatbots. These enhancements invariably lead to increased customer satisfaction, freeing up human agents to tackle more complex customer concerns. Furthermore, ChatGPT's natural language processing prowess can be harnessed to guide customers on suitable insurance products and services, digitizing sales and distribution.The underwriting process, a traditionally time-consuming task characterized by risk evaluation, is another sector ripe for the automation that ChatGPT brings. While Artificial Intelligence (AI) and Machine Learning (ML) models have previously been employed to improve the accuracy of risk assessment, gaps in data and information remain problematic. ChatGPT addresses this issue by enhancing data collection and analysis, investigating digital resources for analogous cases, and speeding up the identification of risk patterns.Through this sophisticated data analysis, ChatGPT can evaluate factors like a customer's age, financial status, occupation, and lifestyle, thereby determining their risk profile. This information enables insurers to offer personalized coverage and pricing, improving customer experience, and streamlining underwriting. In addition, it can alert insurers about high-risk individuals or circumstances, proactively averting potential losses. This automatic evaluation brings with it many questions around AI and ethics (which you can read more about here) but the advantages of getting such a system working are clear.Claims processing, an insurance operation infamous for its high cost and low level of digitization, is another area primed for disruption by ChatGPT. The AI model can proficiently extract and categorize information from claims forms and other documents, drastically reducing the laborious and time-intensive task of manual data entry.A significant advantage arising from automating claims processing is its potential in fraud detection. With estimates by the FBI suggesting that insurance fraud costs American families hundreds of dollars each year in premium hikes, the value of early fraud detection cannot be overstated. ChatGPT can help in showing patterns of inconsistencies in claim forms, then flagging suspicious entries for human review. By alerting insurers to both overt and covert attempts at fraud, ChatGPT can help save billions of dollars annually.Reasons to be carefulAs a caveat, while the utility and advantages of ChatGPT in the insurance industry are substantial, one must consider the nascent stage of this technology. Its real-world impact will be contingent on its successful integration within existing processes and wide-ranging adoption. Moreover, while AI systems offer remarkable capabilities, they are not infallible and require human supervision. Thus, the technology's explainability, its transparency, and its limitations should be carefully considered and understood.In summary, the potential of ChatGPT to transform the insurance industry is vast, promising efficiency gains, cost reductions, and enhanced customer service. But realizing these advantages requires industry-wide receptiveness, careful integration, and judicious application, along with a respect for the limitations of the technology.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

The fast-paced development of Artificial Intelligence has already started reshaping various sectors, with the real estate industry standing out as a prominent beneficiary. Of particular interest is the potential AI presents in streamlining property valuation, a critical process that underlies a myriad of real estate activities, including setting sale prices, making investment decisions, and optimizing home insurance premiums. While the conventional means of property valuation have their merits, they are far from perfect. This article delves into the potential of AI, specifically OpenAI's ChatGPT, in transforming property valuation in the real estate sector, discussing the challenges inherent to traditional approaches and exploring the benefits offered by this AI-driven approach.The Current State of Property ValuationProperty valuation is a meticulous process that draws on a variety of data sources, both public and private. Depending on the valuation's purpose, the time and effort committed to research can differ significantly. For instance, real estate brokers might base their Broker Price Opinions on a limited set of comparable properties, while appraisers might undertake a thorough firsthand inspection to understand a property's condition, quality, and value comprehensively.Despite the evolution of valuation methodologies over the years, traditional approaches still grapple with certain obstacles. One of the primary issues is data inconsistency, mainly arising from the dispersed and scattered nature of relevant property data across various sources. While attempts have been made to centralize information on property features, ownership changes, and other key insights, consistency in data remains elusive. The result is disparities in the Automated Valuation Models (AVMs) currently used, which can lead to divergent valuations for the same property.Moreover, human bias forms a significant challenge in property appraisals. It's often difficult to find identical properties for comparison, leading to inevitable subjectivity in adjustments made to reconcile price differences. Studies show that appraised values fall below the agreed purchase price in just 10% of cases, suggesting a propensity towards price confirmation bias, a situation that calls for greater objectivity in home appraisals.Integrating AI into Property Valuation: The Role of ChatGPTIn response to these challenges, the integration of AI into the property valuation process presents a promising solution. The application of AI, especially advanced language models like ChatGPT, can offer consistent examinations of a property’s condition and quality, mitigating issues associated with data inconsistencies and human bias.ChatGPT, a generative pre-trained transformer, has been designed to understand and generate human-like text based on given input. In the context of real estate, it offers tremendous potential in data analysis and, consequently, in generating accurate property valuations. Traditionally, property valuations have been conducted by human appraisers who assess a property’s worth based on a range of factors such as location, size, and condition. However, this approach can be time-consuming, costly, and susceptible to human error.By incorporating ChatGPT into the valuation process, real estate professionals can input relevant data into the AI model, which can then analyze the data and supply a detailed valuation report. The implications of this are transformative for the industry: it offers considerable time savings, reduces the potential for errors, and enhances the transparency of the valuation process.A Practical Application of ChatGPT in Property ValuationHere’s a very simple prompt that illuminates how ChatGPT can be a great guide in the property valuation process:    The evolution of AI has unlocked numerous opportunities for innovation and efficiency across a variety of sectors, with the real estate industry being no exception. Particularly, the advent of AI models like ChatGPT has opened new avenues for enhancing the accuracy and efficiency of property valuations. By surmounting the obstacles inherent to traditional valuation methodologies, such as data inconsistencies and human bias, AI offers a more streamlined, transparent, and precise approach to property valuation. Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

The application of advanced AI tools, such as ChatGPT, in various industries, particularly finance, has proven transformative due to its extensive language processing capabilities. ChatGPT's functions within the financial sector are diverse and impressive. It can understand financial market dynamics, suggest products, identify specific entities, and generate financial summaries, reports, and forecasts. Furthermore, the potential of training ChatGPT for fraud prevention and detection is an exciting prospect.However, as the integration of ChatGPT into the financial services realm becomes more prevalent, it brings to the fore several ethical challenges. Therefore, the onus is on both researchers and practitioners to ensure that the technology's use is responsible and advantageous to all parties involved. The solutions to these ethical challenges often require a multi-faceted approach, focusing on data exposure, misinformation, technology dependency, privacy concerns, and social engineering.The Ethical Challenges InvolvedOne of the paramount ethical challenges is data exposure. For example, ChatGPT users working with financial data might unintentionally disclose sensitive information. Additionally, during the AI model's training phase, there's a risk of exposing confidential elements such as proprietary code snippets, API keys, or login credentials.ChatGPT can sometimes generate biased or inaccurate responses, causing misinformation. The tool, at present, operates based on data sets that only run up to September 2021, which are sourced online and not always accurate. Therefore, financial professionals must exercise caution while using such advice to prevent the propagation of misinformation.Furthermore, while AI can be a powerful tool for financial decision-making, relying solely on technology can undermine human judgment and intuition. Financial professionals could fall into the trap of misinterpreting or overly depending on ChatGPT's advice, thereby overlooking the importance of human expertise in the financial sector. Therefore, it is crucial to strike a balance between utilizing AI's efficiency and maintaining human critical thinking.As ChatGPT requires an extensive amount of data for training, this raises significant privacy concerns. The information collected could pose serious risks to both individuals and organizations if exposed or used maliciously. In tandem with privacy concerns, social engineering issues arise as well. There is a potential for cybercriminals to misuse ChatGPT, impersonating individuals or organizations to conduct successful phishing attacks.Solving the ProblemAddressing these ethical challenges requires robust solutions. Firstly, the co-creation approach, which emphasizes public participation and stakeholder involvement in designing the AI algorithm. This strategy includes key choices in the algorithm, from the scope of its use to mitigating biases and tackling misinformation. It also ensures that humans keep a certain level of control over the AI tool, thus preventing total dependency on the technology.Secondly, the institutional approach can ensure the ethical use of ChatGPT in finance. This approach demands the establishment of concrete rules for managing ChatGPT, including training policy regulators to scrutinize and audit the AI algorithm and developing regulations. The focus is on creating transparent tools that ensure user privacy and constantly upgrade security measures to prevent breaches by cybercriminals.Lastly, it's vital to maintain a harmonious blend of AI-based decision-making and human intuition. While ChatGPT can crunch data and analyze trends with efficiency, human professionals have the experiential knowledge to make intuitive financial decisions. The amalgamation of both AI and human insight can lead to mutual learning and overall improvement in financial decision-making. It can also help address legal obstacles in financial domains that AI might overlook, thus ensuring the accuracy and reliability of financial decisions.The UK Finance paper on AI Fairness in Financial Services recommends a multi-disciplinary approach:Frontline business must be clear on the objective of the use of AI, the risks to individuals and to the business, and the extent to which risks of unfair treatment will be managed and explained to stakeholders.Data scientists are central to the technical aspects of the use, testing and monitoring of AI.Legal and Compliance need to be involved (including in any preliminary stages) to provide appropriate challenge, to oversee testing and to assist with fair process and related transparency principle.In addition, human application can mitigate the looming threat of job loss due to automation. While technology like ChatGPT can automate many functions, it is essential to preserve roles where human intuition, expertise, and judgment are irreplaceable.While the adoption of ChatGPT in finance is indeed a technological advancement, it comes with ethical challenges that require strategic and thoughtful solutions. Companies must adopt strategies such as co-creation and institutional approaches to ensure ethical usage. Furthermore, they need to strike a balance between AI and human insight to maintain the integrity of financial decisions. By addressing these challenges and implementing relevant strategies, we can ensure a future where AI not only augments the financial sector but also respects the values that we hold dear.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

In the realm of Artificial Intelligence, immense potential is no longer an abstract concept but a palpable reality, and businesses are increasingly seizing the opportunities this technology affords. AirOps, a new player in the AI sphere, has emerged as a remarkable conduit for businesses to harness the transformative abilities of AI within their operations. The company has announced a $7 million seed funding round, showing the confidence investors place in its unique proposition.Founded by Alex Halliday, Berna Gonzalez, and Matt Hammel, the company encapsulates a blend of technological knowledge and industry expertise. Their collective backgrounds span a diverse range of sectors, including MasterClass, Bungalow, and more. This multifaceted perspective fuels the vision of AirOps, allowing it to offer dynamic and adaptable solutions tailored to a multitude of business needs.AirOps deploys a platform leveraging large language models (LLMs) such as GPT-3, GPT-4, and Claude, each with its unique capabilities and merits. The AI-driven tools developed by AirOps can be integrated within existing business systems, speeding up processes, revealing deep insights from data, and generating custom content. These services are readily available across various interfaces, including Google Sheets, web apps, data warehouses, or APIs, thereby allowing businesses to embed AI capabilities directly into their established workflows.Airops' Main FeaturesDespite the impressive abilities of LLMs like GPT-4, the challenge for businesses lies in their practical deployment. AirOps mitigates this hurdle, offering a robust platform that enables businesses to use these AI models in addressing their specific needs. The platform helps users automate laborious tasks, generate personalized content, extract valuable insights from data, and leverage natural language processing techniques.One of the salient features of AirOps' value proposition is cost efficiency. Utilizing AI models can often be a costly endeavor, but the AirOps platform presents an innovative solution. The system employs larger models such as GPT-4 for initial training, then switches to smaller, fine-tuned, open-source models for regular operations, significantly reducing the financial burden.As AI evolves, the demand for nuanced and adaptable models increases. AirOps is at the forefront of these developments, continually learning and adapting to offer the most suitable solutions for its customers. AirOps aids businesses in creating AI experiences and generating new content from their existing data corpus, paving the way for a streamlined and efficient approach to making the most of AI capabilities.The company's strategic vision is also worth noting. Initially, AirOps set out to help businesses in extracting value from their data. However, as large language models have gained public recognition, the company has astutely shifted its focus. Today, AirOps aims to facilitate businesses in merging their data with LLMs, leading to the creation of custom workflows and applications.As AI continues to permeate the professional sphere, AirOps is showing how businesses can capitalize on this trend. Their AI-powered tools are being used across a variety of sectors, such as real estate, e-learning, and financial services, among others. By automating complex tasks, streamlining workflows, and generating custom content at scale, AirOps is empowering businesses to harness the transformative capabilities of AI effectively and efficiently.With its recent seed funding, the company aims to expand its product suite, bolster its team, and extend its customer base. As Halliday, the CEO, stated, the company's goal is to enable businesses to bridge the gap between the theoretical prowess of AI and its practical implementation. Through its groundbreaking work, AirOps is ensuring that the AI revolution in the business world is not merely a utopian vision, but an attainable reality.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

Artificial Intelligence continues to infiltrate every facet of modern life, from daily chores to complex decision-making procedures, including the stock market. The recent advent of AI-powered language models like ChatGPT by OpenAI serves as a notable testament to this statement. The potential of these models transcends conversational prowess, delving into the ability to guide investment decisions.A case in point is an experiment conducted by Finder.com, an international financial comparison site. The test pitted an AI-constructed portfolio against some of the most renowned investment funds in the United Kingdom, seeing the AI-curated selection outstrip its counterparts. The portfolio, an assortment of 38 stocks picked by ChatGPT, manifested a gain of 4.9% between March 6 and April 28. In comparison, ten top-tier investment funds noted an average decline of 0.8% in the same period. To put this into perspective, the S&P 500 index, an esteemed gauge of the American market, marked a rise of 3%, and the Stoxx Europe 600, its European equivalent, noted a modest increase of 0.5%.The experiment's dynamics are as intriguing as its outcome. Investment funds aggregate capital from a multitude of investors, a fund manager administering the investment decisions. However, Finder's analysts asked the AI chatbot to construct a stock portfolio based on prevalent selection criteria - low indebtedness and a solid growth trajectory. Noteworthy picks included industry behemoths like Microsoft, Netflix, and Walmart.This process's ingenuity lies in its accessibility. While AI has pervaded major funds for years, supplementing investment decisions, the advent of ChatGPT has democratized this expertise. Now, the public can use this technology, thereby revolutionizing retail investment.How dependable are these AI-driven stock predictions? A study by the University of Florida supplies an answer. Published in April, the study posits that ChatGPT could forecast specific companies' stock price movements more accurately than some fundamental analysis models.In fact, the democratization of AI, characterized by models like ChatGPT and BERT, could potentially upend the financial industry. Researchers across the globe have corroborated this sentiment. In two separate studies, researchers found that large language models (LLMs) can enhance stock market and public opinion predictions, evidenced by historical data.University of Florida professors Alejandro Lopez-Lira and Yuehua Tang further validated this argument in their paper "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models". They utilized ChatGPT to assess news headlines' sentiment, a metric that has become indispensable for quantitative analysis algorithms employed by stock traders.Sentiment analysis discerns whether a text, such as a news headline, conveys a positive, neutral, or negative sentiment about a subject or company. This evaluation enhances the accuracy of market predictions.Lopez-Lira and Tang applied ChatGPT to gauge the sentiment manifested in news headlines. Upon comparing ChatGPT's assessment of these news stories with the subsequent performance of company shares in their sample, they discovered statistically significant predictions, a feat unachieved by other LLMs.The professors asserted, "Our analysis reveals that ChatGPT sentiment scores exhibit a statistically significant predictive power on daily stock market returns." This statement, substantiated by their findings, shows a strong correlation between the ChatGPT evaluation and the subsequent daily returns of the stocks in their sample. It underscores the potential of ChatGPT as a potent tool for predicting stock market movements based on sentiment analysis.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

OpenAI’s ChatGPT has gained significant attention since it first launched, and with its versatile capabilities and high accuracy, it has the potential to make a substantial impact on the crypto market. It’s crucial that we explore how AI and Natural Language Processing (NLP) can assist in fraud detection and prevention, understand the capabilities and limitations of ChatGPT in the crypto industry and trading, highlight the importance of AI in safeguarding the crypto market, and discuss ChatGPT's role in crypto compliance, AML, security, and its future implications.AI and NLP for Fraud Detection and PreventionNLP is a branch of AI that enables machines to read, understand, and draw conclusions from human languages. By using computational linguistics and statistical models, NLP can show suspicious behavior patterns and uncover fraud in financial transactions. For instance, NLP can detect inconsistencies in credit applications or identify suspicious transactions on credit cards.Capabilities and Limitations of ChatGPT in the Crypto IndustryIn the crypto industry, ChatGPT has various applications, particularly in trading. It can supply a historical overview of a certain type of cryptocurrency, analyze market data, forecast price movements, create strategies, and find trading opportunities. By leveraging ChatGPT, traders can make better-informed decisions and capitalize on emerging possibilities.Here is a very simple example of how ChatGPT can aid in creating a strategy for identifying Chainlink bottoms using the relative strength index (RSI), support and resistance levels, and moving averages:While ChatGPT can elucidate various aspects of the crypto arena, it's imperative to recognize its potential limitations, particularly pertaining to the source reliability of its information. The internet is fraught with misinformation, and since the advent of GPT-4 that offers web-browsing capabilities, such misinformation could inadvertently affect AI tools like ChatGPT. Within the volatile crypto market, such unreliable information can lead to imprudent investments. It’s advisable to fact-check the data ChatGPT provides to mitigate the risk of utilizing information from dubious sources.The Importance of AI in Safeguarding the Crypto MarketThe adoption of blockchain technology has brought benefits such as increased openness, data consistency, and security. By integrating AI with blockchain, a more secure and intelligent system can be established. Blockchain ensures the integrity of shared information and models used by AI, while AI enhances fraud detection capabilities. The combination of AI and blockchain creates a more resilient system that is resistant to attacks and fraud.ChatGPT in Crypto Fraud DetectionChatGPT, with its NLP capabilities, can contribute to fraud detection in the crypto market in several ways:Identifying Suspicious Transactions and Activities: By analyzing emails for suspicious language patterns and detecting anomalies, ChatGPT can help identify potential fraud. It can compare email texts to earlier communications from the same individual, ensuring consistency and detecting deviations.Analyzing Patterns and Anomalies in Crypto Trading Data: ChatGPT can analyze market data and find significant patterns and trends that can aid traders in making informed decisions.Monitoring Social Media and External Sources: ChatGPT can help compliance teams in monitoring chat and social networking platforms for suspicious activities, such as market manipulation and insider trading.Utilizing Advanced Machine Learning Algorithms for Risk Assessment: Machine learning algorithms can predict the likelihood of default on loans or identify risky transactions. This information helps lenders make more informed decisions and manage risks effectively.ChatGPT in Crypto Compliance and AMLIdentifying and Verifying the Identity of Crypto Traders and Investors: ChatGPT excels in identifying and verifying the identity of traders and investors, ensuring the authenticity of individuals involved in crypto transactions.Monitoring for Money Laundering and Financial Crimes: By leveraging AI capabilities, compliance teams can monitor transactions and identify suspicious patterns indicative of money laundering or other financial crimes.Keeping Up with Regulatory Changes and Compliance Requirements: AI chatbots like ChatGPT can adapt to regulatory changes and comply with requirements set by authorities to ensure seamless operations within legal frameworks.Developing and Implementing Effective KYC and AML Procedures: NLP and monitored machine learning techniques play a vital role in streamlining Know Your Customer (KYC) procedures. These technologies facilitate efficient identity verification and analysis of unstructured content.ChatGPT in Crypto SecurityProtecting Crypto Assets and Digital Wallets: AI tools like ChatGPT enhance security measures in crypto exchanges and platforms, safeguarding digital assets and wallets from potential threats.Enhancing Security in Crypto Exchanges and Platforms: ChatGPT helps in verifying the identities of investors, bolstering the overall security mechanism of crypto exchanges and platforms.Identifying and Preventing Phishing and Hacking Attempts: AI algorithms can block unauthorized smart contracts and reduce the risk of phishing and hacking attacks, thereby enhancing the security of the crypto industry.Developing and Implementing Advanced Security Protocols: AI algorithms and machine learning techniques help organizations identify vulnerabilities in their security architecture and improve overall system security.Future Developments and Implications of ChatGPT in CryptoAdvancements in NLP and AI are expected to have a significant impact on fraud detection and prevention. As society moves toward a cashless economy, the role of AI in identifying and preventing digital fraud becomes increasingly critical. ChatGPT's ability to fine-tune popular themes enables traders to stay updated on crypto news, retrieve relevant data, and generate trading strategies based on historical information. However, it is crucial to consider the ethical implications and potential risks associated with using AI for fraud detection and in financial systems. Responsible and informed use of AI technologies can contribute to building trust and credibility in the crypto market.ChatGPT, with its advanced NLP capabilities, offers exciting possibilities for the crypto market. Its potential to enhance fraud detection, bolster security measures, and build trust and credibility is promising. However, it is essential to approach AI adoption in the crypto market cautiously, taking into account ethical considerations and potential implications. As technology continues to evolve, the responsible and informed use of AI can pave the way for a safer and more efficient crypto ecosystem.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

Are you weary of trawling through heaps of analysis data in search of meaningful insights? With ChatGPT, the rules will soon alter. ChatGPT may reveal hidden patterns and trends in your data that you never imagined were there because of its sophisticated natural language processing skills. In this blog article, we'll look at how exploratory data analysis with ChatGPT can revolutionize your data and change the way you conduct business.Data Analysis with ChatGPTFor data analysts, ChatGPT can be a useful tool for processing, exploring, communicating, and collaborating on their data-driven ideas. Large volumes of data can be analyzed and processed by ChatGPT fast and effectively. Written inquiries can be interpreted and understood by ChatGPT through its language processing skills, which also allow it to extract pertinent insights from the data. Here are a few benefits that ChatGPT can provide: Data analysts can use ChatGPT to study their data, spot trends, and even produce useful data visualizations. The data is clearly outlined in these graphics, which makes it simpler for analysts to spot trends and insights. Data analysts can utilize ChatGPT to explain their findings to non-technical stakeholders. The chatbot can assist data analysts in providing simple explanations of complicated data ideas and insights by using natural language. Data analysts might benefit from ChatGPT's help in coming up with fresh, insightful queries to pose to their data. Analysts can investigate novel lines of inquiry and unearth previously unconsidered hidden insights by using natural language queries. Let's look at how chatGPT can make data analysis easy and straightforward. As a data modeler, I want to investigate the data's dictionary and metadata first. Image 1: : Data Dictionary Using ChatGPT, Part 1Image 2 : Data Dictionary Using ChatGPT, Part 2ChatGPT gives us thorough details about the data dictionary for each column, including a complete description of each column. The final user will benefit from this guidance on when and how to use the data.Asking chatGPT about the dataset's number of rows and columns will help you better grasp the overall statistics.  Image 2 : Dataset Statistics  As seen in the image above, chatGPT gives us a precise estimate of the dataset's number of rows and columns. After getting a broad overview of the dataset, let's examine the data's quality: Image 3 : Exploratory Data Analysis - Null Value StatisticsHere, we've given the chatGPT an input containing the dataset and requested it to determine the percentage of null values therein in order to determine whether the data can be used for analytics. The dataset does not contain any null values, hence chanGPT responds that the given dataset contains no missing values.Now, we can observe that the data set's header information is absent. Before we can use the data, the columns must contain meaningful data. Image 4 : Dataset Column Naming ConventionLet's ask chatGPT how it can deliver valuable header data. As you can see, the output of chatGPT is a column header with a description and business-specific naming standards. The technical team's and business users' lives are made easier in terms of using this data.We now know that the data quality is good. As this will affect the results of the data analysis, let's look for any outliers in the dataset. Image 5 : Detect Outliers in the DatasetIn this case, chatGPT is carrying out an in-depth analysis at the column level to see whether any outliers are there. It's okay if it doesn't exist. If it does, it also offers advice on what kind of outlier is present and how it can affect the entire data analysis procedure.Let's now look at how to use chatGPT to eliminate those outliers.Image 7 : Remove Outliers from the dataset using python, Part 1 Image 8 : Remove Outliers from the dataset using python, Part 2Therefore, for a given sample dataset, ChatGPT offers a thorough Python code that can be used to automatically eliminate the observed outliers. The team may have business analysts who are unfamiliar with Python. Let's see how chatGPT can assist business analysts with their data analysis work.Image 7 : SQL Query to calculate monthly revenue, Part 1Image 8 : SQL Query to calculate monthly revenueIn this case, chatGPT offers a default query that the business analyst may utilize to figure out the monthly income for a particular dataset. Let's then ask chatGPT to take on the role of a data analyst and offer further insights for a certain dataset. Image 8 : Step by Step Data Analysis using chatGPT, Part 1  Image 9 : Step-by-Step Data Analysis using ChatGPT, Part 2As we can see from the chatGPT's results, it offers us step-by-step advice on various studies and results that may be applied on top of this particular dataset. The execution of each of these tasks using chatGPT is possible for each phase of the overall data analysis process.Let's ask chatGPT to undertake this data analysis work so that it may use Python to analyze prices for the given dataset:Image 9 : Price Analysis using python, Part 1Image 10 : Price Analysis using python, Part 2Image 11 : Price Analysis using Python, Part 2For the purpose of doing price analysis on a given dataset, ChatGPT has developed a Python code and sample output. We can draw a judgment about how the prices are changing over time based on the data points at hand from this output.ConclusionIn this article, we go into great detail on how to use chatGPT for a variety of exploratory data analysis tasks. Additionally, we looked closely at different approaches to carrying out data analysis tasks using Python and SQL. ChatGPT is, in a word, a very useful tool for performing exploratory data analysis tasks while working with massive volumes of data.Author BioSagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP. Link - Medium , Amazon , LinkedIn 

Adobe Firefly Text EffectsAdobe Firefly is a new set of generative AI tools which can be accessed via https://firefly.adobe.com/ by anyone with an Adobe ID. To learn more about Firefly… have a look at their FAQ.  Image 1: Adobe FireflyOne of the more unique aspects of Firefly that sets it apart from other generative AI tools is Adobe’s exploration of procedures that go beyond prompt-based image generation. A good example of this is what is called Text Effects in Firefly.Text effects are also prompt-based… but use a scaffold determined by font choice and character set to constrain a generated set of styles to these letterforms. The styles themselves are based on user prompts – although there are other variants to consider as well.In the remainder of this article, we will focus on the text-to-image basics available in Firefly.Using Text Effects within FireflyAs mentioned in the introduction, we will continue our explorations of Adobe Firefly with the ability to generate stylized text effects from a text prompt. This is a bit different from the procedures that users might already be familiar with when dealing with generative AI – yet retains many similarities with such processes.When you first enter the Firefly web experience, you will be presented with the various workflows available.Image 2: Firefly modules can be either active and ready to work with or in explorationThese appear as UI cards and present a sample image, the name of the procedure, a procedure description, and either a button to begin the process or a label stating that it is “in exploration”. Those which are in exploration are not yet available to general users.We want to locate the Text Effects module and click Generate to enter the experience.Image 3: The Text effects module in FireflyFrom there, you’ll be taken to a view that showcases text styles generated through this process. At the bottom of this view is a unified set of inputs that prompt you to enter the text string you want to stylize… along with the invitation to enter a prompt to “describe the text effects you want to generate”.Image 4: The text-to-image prompt requests your input to beginIn the first part that reads Enter Text, I have entered the text characters “Packt”. For the second part of the input requesting a prompt, enter the following: “futuristic circuitry and neon lighting violet”Click the Generate button when complete. You’ll then be taken into the Firefly text effects experience.  Image 5: The initial set of four text effect variants is generated from your prompt with the characters entered used as a scaffoldWhen you enter the text effects module properly, you are presented in the main area with a preview of your input text which has been given a stylistic overlay generated from the descriptive prompt. Below this are a set of four variants, and below that are the text inputs that contain your text characters and the prompt itself.To the right of this are your controls. These are presented in a user-friendly way and allow you to make certain alterations to your text effects. We’ll explore these properties next to see how they can impact our text effect style.Exploring the Text Effect PropertiesAlong the right-hand side of the interface are properties that can be adjusted. The first section here includes a set of Sample prompts to try out.Image 6: A set of sample prompts with thumbnail displaysClicking on any of these sample thumbnails will execute the prompt attributed to it, overriding your original prompt. This can be useful for those new to prompt-building within Firefly to generate ideas for their own prompts and to witness the capabilities of the generative AI. Choosing the View All option will display even more prompts.Below the sample prompts, we have a very important adjustment that can be made in the form of Text effects fit.Image 7: Text effects fit determines how tight or loose the visuals are bound to the scaffoldThis section provides three separate options for you to choose from… Tight, Medium, or Loose. The default setting is Medium and choosing either of the other options will have the effect of either tightening up all the little visual tendrils that expand beyond the characters – or will let them loose, generating even more beyond the bounds of the scaffold.Let’s look at some examples with our current scaffold and prompt:Image 8: Tight - will keep everything bound within the scaffold of the chosen charactersImage 9: Medium - is the default and includes some additional visuals extending from the scaffoldImage 10: Loose - creates many visuals beyond the bounds of the scaffoldOne of the nice things about this set is that you can easily switch between them to compare the resulting images and make an informed decision.Next, we have the ability to choose a Font for the scaffold. There are currently a very limited set of fonts to use in Firefly. Similar to the sample prompts, choosing the View All option will display even more fonts.Image 11: The font selection propertiesWhen you choose a new font, it will regenerate the imagery in the main area of the Firefly interface as the scaffold must be rebuilt.I’ve chosen Source Sans 3 as the new typeface. The visual is automatically regenerated based on the new scaffold created from the character structure.Image 12: A new font is applied to our text and the effect is regeneratedThe final section along the right-hand side of the interface is for Color choices. We have options for Background Color and for Text Color. Image 13: Color choices are the final properties sectionThere are a very limited set of color swatches to choose from. The most important is whether you want to have the background of the generated image be transparent or not.Making Additional ChoicesOkay – we’ll now look to making final adjustments to the generated image and downloading the text effect image to our local computer. The first thing we’ll choose is a variant – which can be found beneath the main image preview. A set of 4 thumbnail previews are available to choose from.Image 14: Selecting from the presented variantsClicking on each will change the preview above it to reveal the full variant – as applied to your text effect.For instance, if I choose option #3 from the image above, the following changes would result:Image 15: A variant is selected and the image preview changes to matchOf course, if you do not like any of the alternatives, you can always choose the initial thumbnail to revert back.Once you have made the choice of variant, you can download the text effect as an image file to your local file system for use elsewhere. Hover over the large preview image and an options overlay appears.Image 16: A number of options appear in the hover overlay, including the download optionWe will explore these additional options in greater detail in a future article. Click the download icon to begin the download process for that image.As Firefly begins preparing the image for download, a small overlay dialog appears.Image 17: Content credentials are applied to the image as it is downloadedFirefly applies metadata to any generated image in the form of content credentials and the image download process begins.What are content credentials? They are driven as part of the Content Authenticity Initiative to help promote transparency in AI. This is how Adobe describes content credentials in their Firefly FAQ:Content Credentials are sets of editing, history, and attribution details associated with content that can be included with that content at export or download. By providing extra context around how a piece of content was produced, they can help content producers get credit and help people viewing the content make more informed trust decisions about it. Content Credentials can be viewed by anyone when their respective content is published to a supporting website or inspected with dedicated tools. -- AdobeOnce the image is downloaded, it can be viewed and shared just like any other image file.Image 18: The text effect image is downloaded and ready for useAlong with content credentials, a small badge is placed upon the lower right of the image which visually identifies the image as having been produced with Adobe Firefly (beta).There is a lot more Firefly can do, and we will continue this series in the coming weeks. Keep an eye out for an Adobe Firefly deep dive… exploring additional options for your generative AI creations!Author BioJoseph is a Teaching Assistant Professor, Instructor of Technology, University of Colorado Boulder / Adobe Education Leader / Partner by DesignJoseph Labrecque is a creative developer, designer, and educator with nearly two decades of experience creating expressive web, desktop, and mobile solutions. He joined the University of Colorado Boulder College of Media, Communication, and Information as faculty with the Department of Advertising, Public Relations, and Media Design in Autumn 2019. His teaching focuses on creative software, digital workflows, user interaction, and design principles and concepts. Before joining the faculty at CU Boulder, he was associated with the University of Denver as adjunct faculty and as a senior interactive software engineer, user interface developer, and digital media designer.Labrecque has authored a number of books and video course publications on design and development technologies, tools, and concepts through publishers which include LinkedIn Learning (Lynda.com), Peachpit Press, and Adobe. He has spoken at large design and technology conferences such as Adobe MAX and for a variety of smaller creative communities. He is also the founder of Fractured Vision Media, LLC; a digital media production studio and distribution vehicle for a variety of creative works.Joseph is an Adobe Education Leader and member of Adobe Partners by Design. He holds a bachelor’s degree in communication from Worcester State University and a master’s degree in digital media studies from the University of Denver.Author of the book: Mastering Adobe Animate 2023 

Advanced language models have been used in the last couple of years to create a variety of AI products, including conversational AI tools and AI assistants. A web-based platform called AgentGPT allows users to build and use AI agents right from their browsers. Making AgentGPT available to everyone and promoting community-based collaboration are its key goals.ChatGPT provides accurate, meaningful, in-depth specific answers and discussion to given input questions while AgentGPT, on the other hand, is an AI agent platform that takes an objective and achieves the goal by thinking, learning, and taking actions.AgentGPT can assist you with your goals without installing and downloading. You just need to create an account and get the power of AI-enabled Conversational AI. You have to provide a name and objective for your agent, and the agent will achieve the goal.What is AgentGPT?AgentGPT is an open-source platform that is developed by openAI and uses the GPT3.5 architecture. AgentGPT is an NLP-based technology that generates human-like text with accuracy and fluency. It can engage in conversations, question-answers, generative content, and problem-solving assistance.How does AgentGPT work?AgentGPT breaks down a given prompt into smaller tasks, and the agent completes these specific tasks in order to achieve the goal. Its core strength is engaging in real and contextual conversation. It generates dynamic discussions while learning from the large dataset. It recognizes the intentions and responds in a way that is human-like.How to Use Agent GPT?Let’s first create an account on reworkd.ai. After creating the account, deploy the agent by providing the agent's name and objective.In the snapshot below, you can see that we are deploying an agent for Fake News Detection. As a user, we just need to provide two inputs: Name and Goal. For example, in our case, we have provided Fake News Detection as the name and Build Classifier for detecting fake news articles as a goal.Image 1: AgentGPT pageOnce you click the deploy agent. It starts identifying the task and add all the task in the queue. After that one by one, it executes all the tasks.Image 2: Queue of tasksIn the below snapshot, you can see it has completed the 2 tasks and working on the third task(Extract Relevant features). In all the tasks, it has also provided the code samples to implement the task.Image 3: Code samplesOnce your goal is achieved, you can save the results by clicking on the save button in the top-right corner.You can also improve the performance by providing relevant examples, using the ReAct approach for improving the prompting, and upgrading the version from local to Pro version.You also set up AgentGPT on the local machine. For detailed instructions, you can follow this link.SummaryCurrently, AgentGPT is in the beta phase, and the developer community is actively working on its features and use cases. It is one of the most significant milestones in the era of advanced large-language models. Its ability to generate human-like responses opens up potential opportunities for industrial applications such as customer service, content generation, decision support systems, and personal assistance.Author BioAvinash Navlani has over 8 years of experience working in data science and AI. Currently, he is working as a senior data scientist, improving products and services for customers by using advanced analytics, deploying big data analytical tools, creating and maintaining models, and onboarding compelling new datasets. Previously, he was a university lecturer, where he trained and educated people in data science subjects such as Python for analytics, data mining, machine learning, database management, and NoSQL. Avinash has been involved in research activities in data science and has been a keynote speaker at many conferences in India.Link - LinkedIn    Python Data Analysis, Third edition                                            

The world of technology, characterized by an incessant and rapid pace of evolution, is on the cusp of a seismic shift. Historically, the development and control of large language models—a key component in modern artificial intelligence systems—have been dominated by tech industry giants. However, emerging developments show that this might not be the status quo for much longer. The burgeoning field of open-source LLMs presents a potential disruption to the current balance of power in the tech industry, signaling a shift towards a more democratic and inclusive AI landscape.Major tech firms like Microsoft and Google, armed with vast financial resources, have long held the reins of the LLM market. Their position seemed unassailable as recent earnings calls indicated a thriving business built around their AI services. Yet, a leaked internal document from Google has cast a shadow of uncertainty over this seemingly secure stronghold. The central idea gleaned from this document? No company has an unassailable fortress against competition in the realm of LLMs, not even the mighty OpenAI, the organization responsible for the groundbreaking GPT-3.The story of GPT-3 is a pivotal chapter in the annals of AI history. Its 2020 release ignited a spark in the research community, illuminating the tantalizing promise of scale. With 175 billion parameters, GPT-3 showed capabilities that stretched beyond its initial training data. The success of this LLM prompted a surge of interest in the creation of larger, more complex models. This development led to an arms race among AI research labs, producing increasingly massive models such as Gopher, LaMDA, PaLM, and Megatron-Turing.However, this race towards larger LLMs engendered a substantial increase in research and development costs. The staggering financial demands associated with training and running models like GPT-3 created an environment where LLM innovation was essentially confined to the wealthiest entities in tech. With this economic pressure to recoup their considerable investment, these companies began to commercialize their technology, leading to the erection of protective "moats" around their products. These mechanisms of defensibility safeguarded their investments against the competition, obscuring their research and constraining the sharing of intellectual resources.Key elements of these moats included the proprietary control over training data, model weights, and the costs associated with training and inference. With their deep pockets, big tech companies kept the upper hand in managing the expenses tied to training and running large LLMs. This dominance rendered even open-source alternatives such as BLOOM and OPT175-B largely inaccessible to organizations without the fiscal means to support the hefty demands of these advanced models.The Coming of Open-Source Language ModelsFor a time, this state of affairs painted a bleak picture for the democratization of LLMs, with the field becoming increasingly exclusive and secretive. However, the ebb and flow of innovation and competition that define the tech industry were bound to respond. The open-source community rose to the challenge, their endeavors intensifying following the release of OpenAI's ChatGPT, an instruction-following language model that illustrated the vast potential of LLMs in a multitude of applications.These open-source alternatives are changing the game by proving that performance is not solely a function of scale. Small, nimble LLMs trained on expansive datasets have proven the ability to compete head-to-head with their larger counterparts. Moreover, the open-source models, often consisting of 7-13 billion parameters, can be fine-tuned to remarkable degrees on a modest budget and can run on consumer-grade GPUs.One such example, the open-source LLM developed by Meta, known as LLaMA, sparked a wave of similar models like Alpaca and Vicuna. These models, constructed on top of LLaMA, displayed an impressive capability for instruction-following akin to ChatGPT. The subsequent release of Dolly 2.0 by Databricks and Open Assistant further enriched the field by providing commercially usable, instruction-following LLMs that organizations can tailor to their specific needs.The impact of these open-source models is profound. They potentially democratize access to advanced AI systems, reducing the cost of training by using techniques like low-rank adaptation (LoRA) and allowing businesses to incorporate LLMs into their operations at an affordable price. This development poses a significant challenge to the established order, undermining the monopoly of tech giants on LLMs.Nonetheless, the rise of open-source models does not spell the end of cloud-based language models. Despite the democratization they promise, open-source LLMs face significant hurdles, including the prohibitive costs of pre-training. Furthermore, they may not be the best choice for all businesses. Companies without in-house machine learning expertise may still prefer the convenience of out-of-the-box, serverless solutions provided by the likes of Microsoft and Google. The entrenched distribution channels of these tech behemoths also present a formidable barrier for open-source LLMs to overcome.However, the broader implications of the open-source movement in LLMs are unmistakable. It expands the market, opens up novel applications, and puts pressure on tech giants to offer more competitive pricing. By democratizing access to advanced AI, it allows for broader participation in the AI revolution, reducing the concentration of power and innovation within a few wealthy tech companies. As the LLM landscape continues to evolve rapidly, the rise of open-source models will leave an indelible mark on the tech industry.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

In recent times, the entrepreneurial environment has seen a surge in innovation, particularly in the realm of artificial intelligence. Among the stalwarts of this revolution is Neo, a startup accelerator masterminded by Silicon Valley investor Ali Partovi. In a groundbreaking move in March, Neo entered a strategic partnership with renowned AI research organization OpenAI, and tech giant Microsoft Corp. The objective was clear: to offer no-cost software and expert advice to startups orienting their focus towards AI. This partnership's results are already tangible, with CassidyAI, a startup championed by content creator Justin Fineberg, being one of the companies benefiting from this initiative.CassidyAI: A Pioneer in AI-Driven Business AutomationFineberg recently announced that CassidyAI is stepping out from the shadows, shedding its stealth mode. CassidyAI's primary function is an embodiment of innovation: facilitating businesses to create customized AI assistants, thus automating tasks, optimizing productivity, and integrating AI across entire organizations. With this aim, CassidyAI is at the forefront of a paradigm shift in business process automation and management.Amplifying Productivity: CassidyAI's VisionAt its core, CassidyAI embraces an ambitious vision: to multiply the productivity of every team within an organization by a factor of ten. This tenfold increase isn't just a lofty goal; it is a transformative approach that involves deploying AI technology across business operations. CassidyAI accomplishes this by providing a platform for generating bespoke AI assistants that cater to individual departmental needs. This process involves training these virtual assistants using the specific knowledge base and data sets of each department.Harnessing AI Across Departments: Versatile Use-CasesThe potential applications of CassidyAI's platform are practically limitless. The diversity of use cases underscores the flexibility and versatility of the AI-assistant creation process. In marketing, for instance, teams can train CassidyAI on their unique writing style and marketing objectives, thereby crafting content that aligns perfectly with the brand image. Similarly, sales teams can enhance their outreach initiatives by leveraging CassidyAI's understanding of the sales pitch, process, and customer profiles.In customer service, AI assistants can respond to inquiries accurately and efficiently, with CassidyAI's ability to access comprehensive support knowledge. Engineering teams can train CassidyAI on their technical stack and engineering methods and architecture, enabling more informed technical decisions and codebase clarity. Product teams can use CassidyAI's profound understanding of their team dynamics and user experience principles to drive product ideation and roadmap collaboration. Finally, HR departments can provide employees with quick access to HR documentation through AI assistants trained to handle such inquiries.Data Security and Transparency: CassidyAI's AssuranceBeyond its vast application range, CassidyAI distinguishes itself through its commitment to data security and transparency. The platform's ability to import knowledge from various platforms ensures a deep understanding of a company's brand, operations, and unique selling propositions. Equally important, all interactions with CassidyAI remain reliable and secure due to their stringent data handling practices and clear citation of sources.Setting Up AI Automation: A No-Code ApproachCassidyAI's approach to implementing AI in businesses is straightforward and code-free, catering to those without programming skills. Businesses begin by securely uploading their internal data and knowledge to train CassidyAI on their unique products, strategies, processes, and more. They then construct AI assistants that are fine-tuned for their distinct use cases, without the need to write a single line of code. Once the AI assistants are ready, they can be shared across the team, fostering an atmosphere of AI adoption and collaboration throughout the organization.Interestingly, the onboarding process for each company joining CassidyAI is currently personally overseen by Fineberg. Although this may limit the pace of early access, it provides a personalized and detailed introduction to CassidyAI’s capabilities and potential. Companies interested in exploring CassidyAI's offerings can request a demo through their website.CassidyAI represents a revolutionary approach to adopting AI technology in businesses. By creating tailored AI assistants that cater to the specific needs of different departments, it offers an opportunity to substantially improve productivity and streamline operations. Its emergence from stealth mode signals a new era of AI-led business automation and provides an exciting glimpse into the future of work. It is anticipated that as CassidyAI gains traction, more businesses will leverage this innovative tool to their advantage, fundamentally transforming their approach to task automation and productivity enhancement.You can browse the website and request a demo here: https://www.cassidyai.comReal-World Use casesHere are some specific examples of how CassidyAI is being used by real businesses:Centrifuge: Centrifuge is using CassidyAI to originate real-world assets and to securitize them. This is helping Centrifuge to provide businesses with access to financing and to reduce risk.Tinlake: Tinlake is using CassidyAI to automate the process of issuing and managing loans backed by real-world assets. This is helping Tinlake to provide a more efficient and cost-effective lending solution for businesses.Invoice Finance: Invoice Finance is using CassidyAI to automate the process of processing invoices and to provide financing to businesses based on the value of their invoices. This is helping Invoice Finance to provide a more efficient and timely financing solution for businesses.Bondora: Bondora is using CassidyAI to assess the risk of loans and to provide investors with more information about the loans they are considering investing in. This is helping Bondora to provide a more transparent and efficient investment platform for investors.Upstart: Upstart is using CassidyAI to assess the creditworthiness of borrowers and to provide them with more personalized lending terms. This is helping Upstart to provide a more inclusive and affordable lending solution for borrowers.These are just a few examples of how CassidyAI is being used by real businesses to improve their operations and to provide better services to their customers. As CassidyAI continues to develop, it is likely that even more use cases will be discovered.SummaryCassidyAI, a startup in partnership with Neo, OpenAI, and Microsoft, is revolutionizing business productivity through AI-driven automation. Their platform enables businesses to create customized AI-assistants, optimizing productivity and integrating AI across departments. With a no-code approach, CassidyAI caters to various use-cases, including marketing, sales, customer service, engineering, product, and HR. The platform emphasizes data security and transparency while providing a personalized onboarding process. As CassidyAI emerges from stealth mode, it heralds a new era of AI-led business automation, offering businesses the opportunity to enhance productivity and streamline operations.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

AI is often associated with complex algorithms and advanced programming, but for basic linear regression models, Excel is a suitable tool. While Excel may not be commonly linked with AI, it can be an excellent option for building statistical machine-learning models. Excel offers similar modeling capabilities as other libraries, without requiring extensive setup or coding skills. It enables leveraging machine learning for predictive analytics without writing code. This article focuses on using Excel to build a linear regression model for predicting story points completed by a software development team based on hours worked.What is Linear Regression?Before a linear regression model can be built it is important to understand what linear regression is and what it's used for.  For many, their first true shake with linear regression will come in the form of a machine learning library or machine learning cloud service. In terms of modern machine learning, linear regression is a supervised machine learning algorithm that is used for predictive analytics.  In short, linear regression is a very common and easy-to-use machine learning model that is borrowed from the field of statistics.  This means, at its core, linear regression is a statistical analysis technique that models a relationship between two or more variables.  In the most rudimentary sense, linear regression boils down to the following equation,y = mx + bAs can be seen, the equation (that is the linear regression model) is little more than the equation for a line.  No matter the library or machine learning service that is used, in its purest form linear regression will boil down to the above equation.  In short, linear regression is used for predictive, numerical models.  In other words, linear regression produces models that attempt to predict a numerical value.  This could be the weight of a person in relation to their height, the value of a stock in relation to the Dow, or anything similar to those two applications.  As stated before, the model that will be produced for this article will be used to predict the number of story points for a given number of hours worked.Why should Excel be used?Due to the statistical nature of linear regression, Excel is a prime choice for creating linear regression models.  This is especially true if (among other things) one or more of the following conditions are met,The person creating the model does not have a strong computer science or machine learning background. The person needs to quickly produce a model.The data set is very small.If a person simply needs to create a forecasting model for their team, forecast stocks, customer traffic, or whatever it may be, Excel will oftentimes be a better choice than creating a traditional program or using complex machine learning software. With that being established, how would one go about creating a linear regression model?Installing the Necessary Add-insTo build a linear regression model the following will be needed,A working copy of Excel.Analysis ToolPak add-in for Excel.The Analysis ToolPak is the workhorse for this tutorial.  As such, if it is not installed follow the steps in the next section; however, if the add-in is already installed the following section can be skipped.Installing Data Analysis ToolPak1. Click,  File -> Option -> Add-insOnce done the following wizard should appear:Figure 1 – Options Wizard2. Locate Analysis ToolPak and select it.  Once that is done the following popup will appear.Figure 2 – Add-ins WizardFor this tutorial, all that is technically needed is the Analysis ToolPak but it is a good idea to install the VBA add-in as well. 3. Verify the installation by navigating to the Data tab and verifying that the Data Analysis tools are installed.  If everything is installed properly, the following should be visible.  Figure 3 – Data Analysis ToolOnce the Analysis ToolPak is installed a linear regression model can be generated with a few clicks of the mouse. Building a Linear Regression Model to Predict Story Points. Once all the add-ins are installed, create a workbook and copy in the following data:HoursStory Points161315121511134228281830191032114117129251924172315 Before the model can be built the independent and dependent variables must be chosen.  This is a fancy way of determining which column is going to be the input and which is going to be the output for the model.  In this case, the goal is to predict the number of story points for a given number of hours worked. As such, when the model is created the number of hours will be inputted to return the number of predicted story points. This means that the number of hours worked will be the independent variable which will be on the X-Axis of the graph and the number of story points will be the dependent variable which will be on the Y-Axis. As such, to generate the model perform the following steps,1. Navigate to the Data tab and click Data Analysis.  When complete the following popup should appear.Figure 4 – Regression Analysis  Scroll down and select Regression then press the OK button.2. Once step 1 is completed the following wizard should appear.Figure 5 – Regression Setup Input the data the same way it is presented in Figure 5.  Once done The data should be rendered as in Figure 6.Figure 6 – Linear Regression Output.At this point, the linear regression model has been produced.  To make a prediction all one has to do is multiply the number of hours worked by the Hours value in the Coefficient column and add the Intercept value in the Coefficient column to that product. However, it is advisable to generate a trendline and add the line’s equation and the R-Squared value to the chart to make things easier to see.  This can be remedied by simply deleting the predicted dots and adding a trendline like in Figure 7.Figure 7 – TrendlineThe trendline will show the best fit for the model.  In other words, the model will use the equation that governs the trendline to predict a value.  To generate the line’s equation click the arrow button by Trendline and click More Options.  When this is done a sidebar should appear similar to the one in Figure 8.Figure 8 – Format Trendline MenuFrom here select the R-square value checkbox and the Display Equation on chart checkbox. When this is done those values should be displayed on the graph like in Figure 9. Figure 9 – Regression Model with line equation and R-squared valueTo create a prediction, all one has to do is plug in the number of hours for x in the equation and the computed value will be an approximation for the number of story points for the hours worked. Interperting the ModelRegression StatisticsMultiple R0.862529R Square0.743956Adjusted R Square0.722619Standard Error2.805677Observations14Now that the model is generated, how good is it?  This question can be answered with the data that was produced in Figure 6.  However, a whole book could be dedicated to interpreting those outputs, so for this article, the data in the observation group which can be thought of as the high-level summary of the model will be explored.   Consider, the following data:Regression StatisticsMultiple R0.862529R Square0.743956Adjusted R Square0.722619Standard Error2.805677Observations14 The first value is Multiple R or as it is sometimes called the Correlation Coefficient.  This value can range from -1 to 0 or 0 to 1 depending on whether the correlation is negative or positive respectively.  The closer the coefficient is to either -1 or 1 the better. With that, what is the difference between a negative and positive correlation?  Whether a correlation is negative or positive depends on the graph’s orientation which in turn means whether the correlation coefficient is positive or negative.  If the graph is downward oriented the correlation is negative. For these models, the correlation coefficient will be less than 0.  On the other hand, if the graph is upward oriented like the graph produced by the model it is said to have a positive correlation which in turn means the coefficient will be greater than 0.  Consider Figure 10,Figure 10 – Negative and Positive Correlation Ultimately it doesn’t matter if the model has a positive or negative correlation.  All the correlation means is that as one value rises the other will either rise with it or fall.  In terms of the model produced, the Multiple R-value is .86.  All things considered that is a really good correlation coefficient. The next important value to look at is the R-Squared value or the Coefficient of Determination.  This value describes how well the model fits the data.  In other words, it determines how many data points fall on the line.  The R-Squred value will range from 0 to 1.  As such, the closer the value is to 1 the better the model will be.  Though a value as close to 1 is desirable it is naïve to assume that an R-Squared of 1 will ever be achievable.  However, a lower R-Squared value is not necessarily a bad thing.  Depending on what is being measured, what constitutes a “good” R-Squared value will vary.  In the case of this model, the R-Squared is about .74 which means about 74% of the data can be explained by the model.  Depending on the context of the application that can be considered good, but it should be remembered that at most the model is only predicting 74% of what makes up the number of completed story points. Adjusted R-Squred is simply a more precise view of the R-Squared value. In simple terms, the adjusted R-Squared value determines how much of a variation in the dependent variables can be explained by the independent variables. The Adjusted R for this model is .72 which is in line with the R-Squard value.Finally, the Standard Error is the last fitting metric.  In a very simplistic sense, this metric is a measure of precision for the model.  As such, the standard error for this model is about 2.8.  Much like other metrics what constitutes good is subjective.  However, the closer the value is to 0 the more concise the model is. Using the modelNow that the model has been created, what would someone do with it, that is how would they use it?  The answer is surprisingly simple.  The whole model is a line equation.  That line will give an approximation of a value based on the given input.  In the case of this model, a person would input the number of hours worked to try to predict the number of story points. As such, someone could simply input the number of hours in a calculator, add the equation to a spreadsheet, or do anything they want with it.  Put simply, this or any other linear regression model is used by inputting a value or values and crunching the numbers.  For example, the equation rendered was as follows:y = 0.6983x - 1.1457The spreadsheet could be modified to include the followingIn this case, the user would simply have to input the number of hours worked to get a predicted number of story points. The important thing to remember is that this model along with any other regression model is not gospel.  Much like in any other machine learning system, these values are simply estimates based on the data that was fed into it.  This means if a different data set or subset is used, the model can and probably will be different. ConclusionIn summary, a simple Excel spreadsheet was used to create a linear regression model.  The linear regression model that was utilized will probably be very similar to a model generated with dedicated machine learning software.  Does this mean that everyone should abandon their machine-learning software packages and libraries and solely use Excel?  The long and the short of it is no! Excel, much like a library like Scikit-learn or any other, is a tool.  However, for laypersons that don’t have a strong computer science background and need to produce a quick regression model, Excel is an excellent tool to do so. Author BioM.T. White has been programming since the age of 12. His fascination with robotics flourished when he was a child programming microcontrollers such as Arduino. M.T. currently holds an undergraduate degree in mathematics, and a master's degree in software engineering, and is currently working on an MBA in IT project management. M.T. is currently working as a software developer for a major US defense contractor and is an adjunct CIS instructor at ECPI University. His background mostly stems from the automation industry where he programmed PLCs and HMIs for many different types of applications. M.T. has programmed many different brands of PLCs over the years and has developed HMIs using many different tools.Author of the book: Mastering PLC Programming

IntroductionIn the fast-paced digital landscape of today, the fusion of text and visual understanding has become paramount. As technology continues to advance, the integration of text and visuals has become essential for enhancing communication, problem-solving, and decision-making processes. With the advent of technologies like ChatGPT and Visual Foundation Models, we now have the ability to seamlessly exchange images during conversations and leverage their capabilities for various tasks. Microsoft's TaskMatrix system serves as a revolutionary solution that bridges the gap between text and visual understanding, empowering users to harness the combined power of these domains. TaskMatrix is an innovative system developed by Microsoft, designed to facilitate collaboration between ChatGPT and Visual Foundation Models. By seamlessly integrating text and visual inputs, TaskMatrix enables users to enhance their communication, perform image-related tasks, and extract valuable insights from visual data. In this technical blog, we will explore the functionalities, applications, and technical intricacies of TaskMatrix, providing a comprehensive understanding of its potential and the benefits it offers to users. Through an in-depth analysis of TaskMatrix, we aim to shed light on how this system can revolutionize the way we interact with text and visual elements. By harnessing the power of advanced machine learning models, TaskMatrix opens up new possibilities for communication, problem-solving, and decision-making, ultimately leading to improved user experiences and enhanced outcomes. Let us now dive deep into the world of TaskMatrix and uncover its inner workings and capabilities.Understanding TaskMatrixTaskMatrix is an open-source system developed by Microsoft with the aim of bridging the gap between ChatGPT and Visual Foundation Models. It serves as a powerful platform that enables the integration of image-related tasks within conversations, revolutionizing the way we communicate and solve problems. One of the key features of TaskMatrix is its ability to facilitate image editing. Users can now manipulate and modify images directly within the context of their conversations. This functionality opens up new avenues for creative expression and enables a richer visual experience during communication. Furthermore, TaskMatrix empowers users with the capability of performing object detection and segmentation tasks. By leveraging the advanced capabilities of Visual Foundation Models, the system can accurately identify and isolate objects within images. This functionality enhances the understanding of visual content and facilitates better communication by providing precise references to specific objects or regions of interest. The integration of TaskMatrix with ChatGPT is seamless, allowing users to combine the power of natural language processing with visual understanding. By exchanging images and leveraging the domain-specific knowledge of Visual Foundation Models, ChatGPT becomes more versatile and capable of handling diverse tasks effectively. TaskMatrix introduces the concept of templates, which are pre-defined execution flows for complex tasks. These templates facilitate collaboration between different foundation models, enabling them to work together cohesively. With templates, users can execute multiple tasks seamlessly, leveraging the strengths of different models and achieving more comprehensive results. Moreover, TaskMatrix supports both English and Chinese languages, making it accessible to a wide range of users across different linguistic backgrounds. The system is designed to be extensible, welcoming contributions from the community to enhance its functionalities and expand its capabilities.Key Features and FunctionalitiesTaskMatrix provides users with a wide range of powerful features and functionalities that empower them to accomplish complex tasks efficiently. Let's explore some of the key features in detail:Template-based Execution Flows: One of the standout features of TaskMatrix is its template-based approach. Templates are pre-defined execution flows that encapsulate specific tasks. They serve as a guide for executing complex operations involving multiple foundation models. Templates streamline the process and ensure smooth collaboration between different models, making it easier for users to achieve their desired outcomes.Language Support: TaskMatrix supports multiple languages, including English and Chinese. This broad language support ensures that users from various linguistic backgrounds can leverage the system's capabilities effectively. Whether users prefer communicating in English or Chinese, TaskMatrix accommodates their needs, making it a versatile and accessible platform for a global user base.Image Editing: TaskMatrix introduces a unique feature that allows users to perform real-time image editing within the conversation flow. This capability enables users to enhance and modify images seamlessly, providing a dynamic visual experience during communication. From basic edits such as cropping and resizing to more advanced adjustments like filters and effects, TaskMatrix equips users with the tools to manipulate images effortlessly.Object Detection and Segmentation: Leveraging the power of Visual Foundation Models, TaskMatrix facilitates accurate object detection and segmentation. This functionality enables users to identify and locate objects within images, making it easier to reference specific elements during conversations. By extracting valuable insights from visual content, TaskMatrix enhances the overall understanding and communication of complex concepts.Integration with ChatGPT: TaskMatrix seamlessly integrates with ChatGPT, a state-of-the-art language model developed by OpenAI. This integration enables users to combine the power of natural language processing with visual understanding. By exchanging images and leveraging the strengths of both ChatGPT and TaskMatrix, users can address a wide range of tasks and challenges, ranging from creative collaborations to problem-solving scenarios.Technical ImplementationTaskMatrix utilizes a sophisticated technical implementation that combines the power of machine learning models, APIs, SDKs, and specialized frameworks to seamlessly integrate text and visual understanding. Let's take a closer look at the technical intricacies of TaskMatrix.Machine Learning Models: At the core of TaskMatrix are powerful machine learning models such as ChatGPT and Visual Foundation Models. ChatGPT, developed by OpenAI, is a state-of-the-art language model that excels in natural language processing tasks. Visual Foundation Models, on the other hand, specialize in visual understanding tasks such as object detection and segmentation. TaskMatrix leverages the capabilities of these models to process and interpret both text and visual inputs.APIs and SDKs: TaskMatrix relies on APIs and software development kits (SDKs) to integrate with the machine learning models. APIs provide a standardized way for TaskMatrix to communicate with the models and send requests for processing. SDKs offer a set of tools and libraries that simplify the integration process, allowing TaskMatrix to seamlessly invoke the necessary functionalities of the models.Specialized Frameworks: TaskMatrix utilizes specialized frameworks to optimize the execution and resource management of the machine learning models. These frameworks efficiently allocate GPU memory for each visual foundation model, ensuring optimal performance and fast response times, even for computationally intensive tasks. By leveraging the power of GPUs, TaskMatrix can process and analyze images with speed and accuracy.Intelligent Routing: TaskMatrix employs intelligent routing algorithms to direct user requests to the appropriate model. When a user engages in a conversation that involves an image-related task, TaskMatrix analyzes the context and intelligently determines which model should handle the request. This ensures that the right model is invoked for accurate and relevant responses, maintaining the flow and coherence of the conversation.Seamless Integration: TaskMatrix seamlessly integrates the responses from the visual foundation models back into the ongoing conversation. This integration ensures a natural and intuitive user experience, where the information and insights gained from visual analysis seamlessly blend with the text-based conversation. The result is a cohesive and interactive communication environment that leverages the combined power of text and visual understanding.By combining machine learning models, APIs, SDKs, specialized frameworks, and intelligent routing algorithms, TaskMatrix achieves a technical implementation that seamlessly integrates text and visual understanding. This implementation optimizes performance, resource management, and user experience, making TaskMatrix a powerful tool for enhancing communication, problem-solving, and collaboration. System Architecture:Image 1: System ArchitectureGetting Started with TaskMatrixTo get started with TaskMatrix, you can follow the step-by-step instructions and documentation provided in the TaskMatrix GitHub repository. This repository serves as a central hub of information, offering comprehensive guidelines, code samples, and examples to assist users in setting up and utilizing the system effectively. Access the GitHub Repository: Begin by visiting the TaskMatrix GitHub repository, which contains all the necessary resources and documentation. You can find the repository by searching for "TaskMatrix" on the GitHub platform.Follow the Setup Instructions:The repository provides clear instructions on how to set up TaskMatrix. This typically involves installing the required dependencies, configuring the APIs and SDKs, and ensuring the compatibility of the system with your development environment. The setup instructions will vary depending on your specific use case and the programming language or framework you are using.# clone the repo git clone https://github.com/microsoft/TaskMatrix.git # Go to directory cd visual-chatgpt # create a new environment conda create -n visgpt python=3.8 # activate the new environment conda activate visgpt #  prepare the basic environments pip install -r requirements.txt pip install  git+https://github.com/IDEA-Research/GroundingDINO.git pip install  git+https://github.com/facebookresearch/segment-anything.git # prepare your private OpenAI key (for Linux) export OPENAI_API_KEY={Your_Private_Openai_Key} # prepare your private OpenAI key (for Windows) set OPENAI_API_KEY={Your_Private_Openai_Key} # Start TaskMatrix ! # You can specify the GPU/CPU assignment by "--load", the parameter indicates which # Visual Foundation Model to use and where it will be loaded to # The model and device are separated by underline '_', the different models are separated by comma ',' # The available Visual Foundation Models can be found in the following table # For example, if you want to load ImageCaptioning to cpu and Text2Image to cuda:0 # You can use: "ImageCaptioning_cpu,Text2Image_cuda:0" # Advice for CPU Users python visual_chatgpt.py --load ImageCaptioning_cpu,Text2Image_cpu # Advice for 1 Tesla T4 15GB  (Google Colab)                      python visual_chatgpt.py --load "ImageCaptioning_cuda:0,Text2Image_cuda:0"                               # Advice for 4 Tesla V100 32GB                           python visual_chatgpt.py --load "Text2Box_cuda:0,Segmenting_cuda:0,    Inpainting_cuda:0,ImageCaptioning_cuda:0,    Text2Image_cuda:1,Image2Canny_cpu,CannyText2Image_cuda:1,    Image2Depth_cpu,DepthText2Image_cuda:1,VisualQuestionAnswering_cuda:2,    InstructPix2Pix_cuda:2,Image2Scribble_cpu,ScribbleText2Image_cuda:2,    SegText2Image_cuda:2,Image2Pose_cpu,PoseText2Image_cuda:2,    Image2Hed_cpu,HedText2Image_cuda:3,Image2Normal_cpu,    NormalText2Image_cuda:3,Image2Line_cpu,LineText2Image_cuda:3" Explore Code Samples and Examples: The TaskMatrix repository offers code samples and examples that demonstrate how to use the system effectively. These samples showcase various functionalities and provide practical insights into integrating TaskMatrix into your projects. By exploring the code samples, you can better understand the implementation details and gain inspiration for incorporating TaskMatrix into your own applications.Engage with the Community: TaskMatrix has an active community of users and developers who are passionate about the system. You can engage with the community by participating in GitHub discussions, submitting issues or bug reports, and even contributing to the development of TaskMatrix through pull requests. The community is a valuable resource for support, knowledge sharing, and collaboration.DemoExample 1:Image 2: Demo Part 1  Image 3: Demo Part 2Example 2Image 5: Automatically generated description ConclusionTaskMatrix revolutionizes the synergy between text and visual understanding by seamlessly integrating ChatGPT and Visual Foundation Models. By enabling image-related tasks within conversations, TaskMatrix opens up new avenues for collaboration and problem-solving. With its intuitive template-based execution flows, language support, image editing capabilities, and object detection and segmentation functionalities, TaskMatrix empowers users to efficiently tackle diverse tasks.As the fields of natural language understanding and computer vision continue to evolve, TaskMatrix represents a significant step forward in bridging the gap between text and visual understanding. Its potential applications are vast, spanning industries such as e-commerce, virtual assistance, content moderation, and more. Embracing TaskMatrix unlocks a world of possibilities, where the fusion of text and visual elements enhances human-machine interaction and drives innovation to new frontiers.Author BioRohan Chikorde is an accomplished AI Architect professional with a post-graduate in Machine Learning and Artificial Intelligence. With almost a decade of experience, he has successfully developed deep learning and machine learning models for various business applications. Rohan's expertise spans multiple domains, and he excels in programming languages such as R and Python, as well as analytics techniques like regression analysis and data mining. In addition to his technical prowess, he is an effective communicator, mentor, and team leader. Rohan's passion lies in machine learning, deep learning, and computer vision.LinkedIn

In recent years, the financial industry has experienced a significant surge in the amount and complexity of data. This exponential growth has underscored the need for advanced artificial intelligence models capable of comprehending and processing the specialized language used in finance. Addressing this demand, Bloomberg unveiled BloombergGPT, a revolutionary language model trained on a diverse range of financial data.The Rise of BloombergGPTReleased on March 30th, BloombergGPT represents a groundbreaking development in the financial sector's application of AI technology. By focusing specifically on finance-related tasks, BloombergGPT aims to enhance existing NLP applications employed by Bloomberg, including sentiment analysis, named entity recognition, news classification, and question answering. Furthermore, this sophisticated model holds the promise of unlocking new possibilities for leveraging the vast amounts of data accessible through the Bloomberg Terminal, thereby empowering the firm's customers and fully harnessing the potential of AI in the financial domain.Unleashing the Power of BloombergGPTBloombergGPT boasts two notable capabilities that propel it beyond generic language models. First, it possesses the ability to generate Bloomberg Query Language (BQL), which serves as a query language for accessing and analyzing financial data on the Bloomberg platform. BQL, a powerful and intricate tool, enables various financial tasks such as data searching, analysis, report creation, and insight generation. BloombergGPT's proficiency in transforming natural language queries into valid BQL fosters more intuitive interactions with financial data, streamlining the querying process and enhancing user experience.The second noteworthy feature of BloombergGPT is its capability to provide suggestions for news headlines. This functionality proves invaluable for news applications and aids journalists in constructing compelling and informative newsletters. By inputting paragraphs, BloombergGPT can generate relevant and engaging titles, saving time and enhancing the efficiency of content creation.Training BloombergGPT: A Domain-Specific ApproachTo train BloombergGPT, Bloomberg employed a domain-specific approach, combining their own financial data with augmented online text data. This strategy demonstrates the value of developing language models tailored to specific industries, surpassing the utility of generic models. The training process involved building a dataset of English-language financial documents, incorporating 363 billion financial-specific tokens from Bloomberg's proprietary data assets and an additional 345 billion generic tokens from online text datasets, including The Pile, C4, and Wikipedia.The resulting domain-specific language model, BloombergGPT, comprises an impressive 50 billion parameters and is optimized for financial tasks. Notably, BloombergGPT outperforms popular open-source language models such as GPT-NeoX, OPT, and Bloom in finance-specific tasks. Furthermore, it exhibits remarkable performance in generic language tasks, including summarization, often rivaling the performance of GPT-3 based on Bloomberg's benchmarks.Applications and Advantages:BloombergGPT's introduction opens up a wealth of possibilities for employing language models in the financial technology realm. One such application is sentiment analysis, which enables the assessment of sentiments in articles, particularly those related to individual companies. Automatic entity recognition is another area where BloombergGPT excels, offering the potential for streamlined data extraction and analysis. Additionally, the model is adept at answering financial questions, providing prompt and accurate responses to user inquiries.Bloomberg's news division can leverage BloombergGPT to automatically generate compelling headlines for newsletters, reducing manual effort and improving efficiency. The model's capability to formulate queries in Bloomberg's proprietary query language (BQL) with minimal examples further augments its versatility. Users can interact with BloombergGPT using natural language, specifying their data requirements, and allowing the model to generate the appropriate BQL, expediting data extraction from databases.Shawn Edwards, Bloomberg's Chief Technology Officer, emphasizes the immense value of developing the first language model focused on the financial domain. The domain-specific approach not only allows for the creation of diverse applications but also yields superior performance compared to developing custom models for each specific task. This advantage, coupled with a faster time-to-market, positions BloombergGPT as a game-changer in the finance industry.The Future of BloombergGPT:BloombergGPT's potential extends beyond its current capabilities. As the model continues to train and optimize on financial data, further progress, and advancements are expected. Its application can be broadened to encompass a wider range of financial tasks, ultimately facilitating more accurate and efficient decision-making in the financial industry.BloombergGPT represents a significant milestone in the advancement of financial natural language processing. By addressing the unique language intricacies of the financial industry, this domain-specific language model holds immense potential for revolutionizing how financial data is analyzed, queried, and leveraged. With its impressive 50 billion parameters and exceptional performance in financial NLP tasks, BloombergGPT positions itself as a powerful tool that will shape the future of the finance industry.Use-casesAutomating research tasks: BloombergGPT is being used by researchers at the University of Oxford to automate the task of summarizing large medical datasets. This has allowed the researchers to save a significant amount of time and effort, and it has also allowed them to identify new insights that they would not have been able to find otherwise.Creating content: BloombergGPT is being used by businesses such as Nike and Coca-Cola to create content for their websites and social media channels. This has allowed these businesses to produce high-quality content more quickly and easily, and it has also helped them to reach a wider audience.Improving customer service: BloombergGPT is being used by customer service teams at companies such as Amazon and PayPal to provide customers with more personalized and informative responses. This has helped these companies to improve their customer satisfaction ratings.Generating code: BloombergGPT is being used by developers at companies such as Google and Facebook to generate code for new applications. This has helped these developers to save time and effort, and it has also allowed them to create more complex and sophisticated applications.Translating languages: BloombergGPT is being used by businesses such as Airbnb and Uber to translate their websites and apps into multiple languages. This has helped these businesses to expand into new markets and to reach a wider audience.These are just a few examples of how BloombergGPT is being used in the real world. As it continues to develop, it is likely that even more use cases will be discovered.SummaryIn recent years, the financial industry has faced a surge in data complexity, necessitating advanced artificial intelligence models. BloombergGPT, a language model trained on financial data, represents a groundbreaking development in the application of AI in finance. It aims to enhance Bloomberg's NLP applications, providing improved sentiment analysis, named entity recognition, news classification, and question answering. Notably, BloombergGPT can generate Bloomberg Query Language (BQL) and suggest news headlines, streamlining financial data querying and content creation. By training the model on domain-specific data, BloombergGPT outperforms generic models and offers various applications, including sentiment analysis, entity recognition, and prompt financial question answering. With further advancements expected, BloombergGPT has the potential to revolutionize financial NLP, enabling more accurate decision-making. The model's versatility and superior performance position it as a game-changer in the finance industry, with applications ranging from automating research tasks to improving customer service and code generation.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

Crafting a compelling cover letter is crucial for programmers seeking new opportunities. ChatGPT can give you a great running start. The more you give it to work with in the prompt, the more tailored and accurate the letter will be.Here is what I prompted: Write a cover letter for a resume for a programmer with over 30 years of experience in C, C++ and C#, git, and creating APIs. The programmer has worked in mobile, front end and back end and most recently has been building APIs for a large enterprise code-base using C#, SQL and Dapper, deployed to Azure using Docker. The position he is applying for includes working on back-end systems in C#, though they are advertising it as full-stack.Make it professional but no more than one page. ChatGPT didn't hesitate Here is what it spit out (I've left off the header with name, address, etc.): Dear [Recipient's Name],I am writing to express my interest in the position of a Programmer at [Company Name], as advertised on [Job Board/Company Website]. With over 30 years of experience in programming and a strong background in C, C++, and C#, I am confident in my ability to contribute to your team and help drive the success of your projects.Throughout my career, I have gained extensive knowledge and expertise in a wide range of programming languages and technologies. I have successfully worked in mobile, front-end, and back-end development, adapting to changing industry trends and applying best practices in each domain. Recently, my focus has been on building robust APIs for a large enterprise code-base using C#, SQL, and Dapper. I have also gained expertise in deploying applications to Azure using Docker, ensuring scalability, reliability, and security.One of my key strengths is my ability to create efficient and well-structured code that is maintainable and extensible. I thrive in collaborative environments, where I can contribute to the development of complex back-end systems. I have a solid understanding of software development principles, design patterns, and architectural concepts, allowing me to deliver high-quality solutions that meet business requirements.Throughout my career, I have been involved in the full software development lifecycle, from gathering requirements and designing solutions to implementation, testing, and maintenance. I am well-versed in version control systems like Git and have experience working in Agile development environments, ensuring timely and iterative delivery of software projects.I am excited about the opportunity to join [Company Name] and contribute to its continued success. I believe that my extensive experience in C#, coupled with my skills in building APIs, will enable me to make immediate and meaningful contributions to your back-end systems. Furthermore, my versatility and willingness to learn new technologies make me well-suited for the advertised full-stack position.I would welcome the opportunity to discuss how my skills and experience align with the needs of [Company Name]. Thank you for considering my application. I have attached my resume for your review, and I look forward to the possibility of an interview.Sincerely,[Your Name] Wow! That sounds like a candidate I'd want to talk with. Notice that it added a number of important (and correct) skills that were not in my prompt. It extrapolated what other skills I must have based on what I told it. For example, I didn't mention anything in the third paragraph, yet it provided a very powerful endorsement of these (assumed?) skills.I'm not sure I'd send this cover letter as-is, but with a little editing, it really would be ready to go.Just for an ego boost, I then asked ChatGPT if it knew me. My prompt was "Who is Jesse Liberty" and it spits out a pretty good bio: Jesse Liberty is a well-known software developer, author, and educator in the field of computer programming. He has been actively involved in the software industry for many years, specializing in various programming languages and technologies. Jesse has authored numerous books on software development, covering topics such as C#, .NET, ASP.NET, and mobile app development. He has also contributed to online learning platforms and delivered lectures and workshops at conferences and events. Jesse Liberty is recognized for his expertise and contributions to the programming community. While that was very gratifying, you must be careful. It doesn't always get things right. I asked, "Is Jesse Liberty a Microsoft MVP?" and it responded. As of my knowledge cutoff in September 2021, Jesse Liberty has not been listed as a Microsoft Most Valuable Professional (MVP). However, please note that MVP status can change over time, and it's always best to verify the most up-to-date information from official sources.By switching to ChatGPT4 I got a more accurate response:As of my last training cut-off in September 2021, Jesse Liberty, a noted author, speaker, and expert in .NET and mobile development, has been a Microsoft MVP in the past. However, the Microsoft MVP (Most Valuable Professional) status is a yearly award, and the list of awardees can change from year to year. It's not possible for me to confirm whether Jesse Liberty is a Microsoft MVP as of 2023 because my training data only goes up until September 2021. For the most up-to-date information, please check Microsoft's official MVP website.The biggest risk I've seen with ChatGPT is that it is so much fun it can become a real-time sink, so caveat emptor.Author BioJesse Liberty is a full-time hands-on programmer, specializing in C#, git, and .NET MAUI. He hosts the popular Yet Another Podcast and is the author of more than a dozen best-selling programming books. Liberty is a Certified Xamarin Developer, a Xamarin MVP, and a Microsoft MVP. He was a Technical Evangelist for Microsoft, a Distinguished Software Engineer at AT&T; a Software Architect for PBS, and a Vice President of Information Technology at Citibank, and he was on the teaching staff at Brandeis University. Jesse is a recognized expert and has spoken at conferences worldwide. Links - LinkedIn  .NET MAUI for C# Developers