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Meta Chatbots

Meta to Introduce AI-Powered Chatbots ‘Personas’ in September

Financial Times (FT) recently reported (2 Aug 2023) that Meta, the owner of the tech giant company, is gearing up to launch a series of AI-backed chatbots. These innovative chatbots, set to debut next month, aim to enhance engagement with Meta’s massive user base of nearly four billion users by offering human-like discussions.

The company has been developing persona-based chatbot prototypes, each exhibiting distinct personalities, including an AI impersonating Abraham Lincoln and another providing travel advice with a laid-back surfer style. The primary purpose of these chatbots will be to offer a novel search function, provide recommendations, and entertain users with interactive experiences.

With several tech giants like Microsoft, Google, and Elon Musk’s ventures entering the AI space, Meta’s move into the AI industry is driven by the goal of attracting and retaining users amidst fierce competition from other social media platforms like TikTok and Twitter.

While the AI-backed chatbots present exciting possibilities, experts have raised concerns regarding user data privacy, manipulation, and potential data exploitation. Ravit Dotan, an AI ethics adviser and researcher, pointed out that interacting with chatbots exposes more user data to companies, leading to concerns about privacy and potential manipulation of users’ preferences.

Meta’s CEO, Mark Zuckerberg, envisions these AI agents serving as assistants, coaches, and facilitators for user interactions with businesses and creators. The company also plans to develop AI-powered productivity assistants for internal use and an avatar chatbot in the metaverse in the future.

To address ethical concerns, Meta intends to employ technology to screen users questions and automate checks on chatbot outputs, ensuring appropriate speech and preventing hate speech or rule-breaking vocabulary.

Meta is expected to provide further details about its AI product roadmap during the Connect developer event in September. As the tech industry rapidly advances in the AI race, Meta’s AI-powered chatbots will undoubtedly play a significant role in shaping the future of user interactions and content delivery.

Graft AI Platform

Graft: Empowering Companies with a User-Friendly AI Platform

In a bid to democratize AI development and make it accessible to companies of all sizes, Graft launched its AI development platform in beta last year. Today, the startup celebrates a significant milestone with a $10 million seed investment and an exciting step towards opening the platform to a larger audience of companies.

The brainchild of Graft’s co-founder and CEO, Adam Oliner, the idea for the company emerged while he was overseeing AI at Slack. Oliner recognizes the tremendous potential AI, particularly ChatGPT, holds for businesses. However, he emphasizes the distinction between experimenting with ChatGPT and creating a robust, production-ready AI application.

“The shiny AI toys available do not cater to production needs, and non-experts often find existing platforms cumbersome. Graft aims to bridge this gap by providing a modern, production-grade AI platform accessible to everyone,” explained Oliner to TechCrunch.

Indeed, while large language models like ChatGPT have simplified some aspects of AI development, the journey to creating a fully operational application remains challenging. The complexity of these models, their lack of transparency, and the emergence of new concerns regarding compliance, privacy, and AI ethics have added layers of intricacy.

User-Friendly Apps: Simplifying AI Adoption

In response to these challenges, Graft has introduced a series of user-friendly apps to facilitate customers quick entry into the AI space without the need for starting from scratch. These apps are templatized use cases, allowing users to easily instantiate them into fully functional production use cases with their data.

For instance, some of the current offerings include visual search and identifying customer champions. Graft strives to simplify the onboarding process—users can create a Graft account, choose from pre-defined templates, and seamlessly integrate their data. The company handles the infrastructure, making the application deployment process a breeze.

As Graft secures additional funding, it gears up to open its platform to more companies in a controlled manner. The vision is to make AI development an inclusive process, empowering businesses of all sizes to harness the potential of artificial intelligence effectively. With Graft’s dedication to creating a user-friendly, production-grade AI platform, the future of AI adoption in the business world looks promising.

ChatGPT Custom Instructions

OpenAI Introduces ‘Custom Instructions’ Feature for ChatGPT Users

Tired of repeating instructions to ChatGPT every time you interact with it? OpenAI has come to the rescue with the launch of their new ‘Custom Instructions‘ feature on Thursday. While currently in beta and exclusive to Plus users, this handy tool allows users to save instruction prompts in the chatbot’s memory, streamlining the conversation process.

OpenAI acknowledged user feedback about the inconvenience of starting each ChatGPT conversation from scratch. With ‘Custom Instructions,’ this friction is greatly reduced, saving users from repetitive prompts.

How ‘Custom Instructions’ Works: Tailoring Responses to User Needs

As described in OpenAI’s blog post, users will be prompted to answer two key questions to utilize ‘Custom Instructions.’ The first question is: “What would you like ChatGPT to know about you to provide better responses?” Users can provide information relevant to their specific needs. For instance, a chef using ChatGPT for recipes might respond with: “I am a chef at a Manhattan restaurant.” The second question is: “How would you like ChatGPT to respond?” Users can then specify their desired responses. For example, the chef may type in: “When I ask for recipes, give me a variation of 3-4 recipes from the best cooks in the world, with portions designed for a single serving.”

Currently, the character limit for user responses is set at 1500. The introduction of ‘Custom Instructions’ has been met with a positive response on social media.

However, OpenAI is aware of potential drawbacks during the beta phase. ChatGPT may not always interpret instructions perfectly, sometimes overlooking or misapplying them. In terms of safety, OpenAI addresses concerns by implementing their Moderation API to prevent the storage of instructions that violate their Usage Policies. Additionally, the model can refuse or ignore instructions that lead to policy-violating responses.

Joanne Jang, a strategic product manager at OpenAI, confirmed that the chatbot refused to respond when prompted with harmful queries, demonstrating the safety measures in place.

One limitation of the feature is that users like the chef in the example will need to switch off the custom instructions tool or provide new instructions if they wish to use ChatGPT for other purposes beyond recipes. This might be time-consuming but could potentially be addressed in future updates.

While ‘Custom Instructions’ is currently available in 22 countries, it is not accessible in the UK and EU. Nevertheless, OpenAI plans to expand the feature to all users worldwide in the coming weeks. The new tool promises to enhance the ChatGPT experience, making interactions more efficient and tailored to individual preferences.

Genesis

Google’s Genesis: An AI Tool for News Writing Raises Concerns Among Journalists

Google has engaged in discussions with media organizations under the News Corp umbrella, which includes The New York Times, The Washington Post, and The Wall Street Journal. The purpose of these meetings is to introduce an AI tool named Genesis, designed to produce and write news stories. According to an exclusive report by The New York Times, Google aims to promote journalism productivity through this tool.

Despite Google’s assurance that the AI tool is meant to assist journalists rather than replace them, it has sparked concerns among industry professionals. Some executives present during the pitch expressed discomfort with the AI’s lack of understanding of the effort required to produce accurate news stories.

While AI can support journalists with research tasks, it may struggle to provide credible and original reporting value. The risk of misinformation spreading is a significant concern, as large language models can sometimes produce incorrect information with confidence.

As the media landscape shifts toward AI-generated content and crowdsourced news, the importance of investigative journalism and fact-checking becomes even more critical.

Several media organizations, including Insider, NPR, and The Times, have already started exploring ways to integrate AI tools into their newsrooms. Google’s spokeswoman, Jenn Crider, clarified that the AI tool is intended to handle menial tasks, such as generating headline options, rather than replacing human journalists.

While some argue against outright rejection of Genesis, citing past instances where technology has transformed aspects of journalism, others highlight potential pitfalls. For instance, Joshua Benton, an American journalist and founder of Nieman Journalism Lab, conducted an experiment using ChatGPT, an AI language model. The result revealed that AI-generated reports can be problematic, containing purple prose, racist elements, and ethical concerns.

In conclusion, the introduction of AI tools like Genesis may have the potential to aid journalists in their work, but the concerns surrounding misinformation and ethical issues warrant cautious consideration. The evolution of technology in journalism has shown both promise and challenges, making it essential for the industry to strike a balance between embracing innovation and upholding journalistic integrity.

xAI

Elon Musk Launches xAI: A New Startup Blurring the Lines of His AI Stance

The world’s wealthiest individual, Elon Musk, has recently unveiled his latest startup venture called xAI. Musk has assembled a team of seasoned professionals in the field of artificial intelligence (AI) to establish this new enterprise. However, Musk’s announcement on Twitter provides only limited details about the objectives of the startup, leaving its purpose somewhat ambiguous for now.

This is not Musk’s first involvement in the realm of AI. Back in 2015, he became an investor in OpenAI, a non-profit research lab dedicated to AI exploration. However, Musk gradually distanced himself from the organization, which eventually split into two entities: one focused on profit-driven endeavors, and the other remaining committed to its non-profit nature.

The profit-driven arm of OpenAI gained significant recognition last year with the launch of ChatGPT, a conversational chatbot that has found applications in generating content, ideas, and code for users.

Musk’s Stance on AI Deployment Musk has been vocal about his concerns regarding OpenAI’s trajectory and AI in general. In a recent interview with the BBC, he revealed his longstanding worries about AI safety, spanning over a decade.

During a CNBC interview, Musk raised questions about the transformation of OpenAI from a non-profit, open-source organization to a for-profit entity that began withholding information about its technology. He also expressed caution about OpenAI’s close ties to Microsoft and the potential impact of the latter’s investments on the former’s future growth.

In March, Musk joined other notable figures in signing a letter calling for a six-month pause on Giant AI Experiments. However, Interesting Engineering reported a month later that Musk had established his own AI startup called xAI, which is now taking shape.

What is xAI?

Currently, limited information is available about the specific plans of xAI. The company has a website that lists several individuals with previous experience at prominent companies like DeepMind, OpenAI, Google Research, Microsoft Research, and Tesla, among others, who have now joined the xAI team.

Notable names include Igor Babuschkin, Manuel Kroiss, Yuhuai (Tony) Wu, and Christian Szegedy, who have made significant contributions to advancements in the AI field, such as AlphaCode, Minerva, GPT 3.5, and the upcoming GPT-4.

The announcement confirms that Elon Musk will lead the team, with Dan Hendrycks, the current Director at the Center for AI Safety, serving as the team’s advisor.

xAI has clarified that it operates independently from X Corp, dispelling any notion that it is an AI project directly tied to X (formerly Twitter). However, this statement does not rule out the possibility of future collaboration with Musk’s other ventures like Tesla or X.

More details are expected to emerge as xAI prepares to host a Twitter Spaces chat on Friday, July 14.

Nevertheless, the fundamental question remains: what unique approach does Musk intend to take with a diverse group of individuals who are actively involved in developing the very AI he strongly opposes? Additionally, if his intention is to open-source AI, how does xAI plan to generate revenue?

The Twitter Space event on Friday promises to be an intriguing platform for further insights and discussions.

Bard 40 Languages

Google’s Bard Chatbot Expands to 40 Languages and EU After Privacy Delay

Google’s ChatGPT competitor, Bard, is now available to a wider audience, including the European Union (EU) and users in over 40 languages. The launch was initially delayed due to concerns over data privacy. Bard comes with several new features, although some are currently only available in English.

Google introduced Bard as a response to the growing success of ChatGPT, developed by OpenAI, a company supported by Google’s rival, Microsoft. While Bard was initially accessible for early access in the United States and the United Kingdom in English, it expanded globally in May to 180 countries with support for Japanese and Korean. However, the EU launch was postponed after the Irish Data Protection Commission (DPC) raised privacy concerns. Google has now addressed these concerns and launched Bard in the EU.

New Features and Improved Performance Accompany Bard’s Wider Rollout

According to Bard’s product lead, Jack Krawczyk, and VP of engineering, Amarnag Subramanya, Google actively engaged with experts, policymakers, and privacy regulators during the expansion process. This launch is considered Google’s largest expansion to date, offering support for Arabic, Spanish, Chinese, German, and Hindi. Additionally, Bard is now available in Brazil.

Alongside the expansion, Bard introduces new features focused on enhancing its responses and productivity. Users can now adjust the tone and style of Bard’s responses with options like “simple,” “long,” “short,” “professional,” or “casual.” The text-to-speech AI feature allows Bard to vocalize its responses in over 40 languages, accessible through a sound icon next to the prompt. For productivity, Bard can export Python code to Replit, a browser-based integrated development environment. Users can also include images in prompts for analysis, pin, rename, and resume recent conversations, and easily share Bard’s responses through links.

Google faced challenges with Bard initially, as it struggled to match the quality of responses from ChatGPT and even provided factually incorrect answers with fabricated citations. This led to criticism from Google employees and a drop in the company’s stock. However, Google claims that Bard has improved, particularly in areas like math and programming. It has gained extensions from Google’s apps and services, as well as third-party partners like Adobe. Bard can now explain code, structure data in tables, and include images in its responses.

However, recent reports from Bloomberg highlighted that the contractors who train Bard are often overworked and underpaid, receiving minimal training and rushed to complete complex audits. This follows an earlier report by Insider, which revealed insufficient time given to verify Bard’s most accurate answers. It appears that these issues have not been addressed adequately.

Generative AI on Coding

The Impact of Generative AI on Coding: Enhancing Efficiency and Problem-Solving

Generative AI has captivated the public’s imagination and ignited a tech gold rush. While AI tools that generate natural language prose and visual art have garnered significant attention, the coding capabilities of AI are increasingly intriguing tech professionals. By simply describing a desired program to an AI chatbot, developers can receive executable code within seconds. This advancement both fascinates and unsettles programmers, but the reality is that machines are unlikely to replace human coders anytime soon. We interviewed programmers who utilize generative AI in their work to understand how it is transforming their workflow, uncovering valuable insights about the benefits and limitations of AI in coding.

The Power of AI in Assisting Coders:

Among the generative AI tools frequently employed by developers, ChatGPT from OpenAI and GitHub Copilot, integrated with popular IDEs like Visual Studio, stood out. While both tools generate code based on natural language queries, Copilot and its experimental successor, Copilot X, go beyond conversational models by acting as advanced IDE autocompletes that anticipate developers’ intentions.

Vanessa Freudenberg, co-founder and chief architect at Croquet.io, uses GitHub Copilot in conjunction with Visual Studio Code for her daily coding. She explains the practical workings of the tool.

If I write the line:


    let x = this.leftMargin + this.width / 2;

it will automatically suggest the next line:


    let y = this.topMargin + this.height / 2;

And it knows that it needs to replace “width” and “left” with “height” and “top”. That saves me a lot of typing.

Panickos Neophytou, co-founder and CTO at NetBeez, utilizes Copilot X and ChatGPT extensively in his coding process. He employs two distinct approaches to leverage these tools beyond simple autocompletion. The first approach is systematic, where he provides a detailed description of a well-defined function, including specific inputs, expected outputs, and relevant data models. The AI can infer associations, and Neophytou instructs it to implement the function in a specific language and manner. He suggests defining project management tasks as such prompts.

Additionally, Neophytou finds a more conversational technique effective. While working on a task, he poses questions that arise in his mind, simulating the experience of having an experienced engineer by his side, answering queries and guiding him toward completion.

Mastering the art of prompting the AI correctly is crucial, regardless of the technique employed. Shanea Leven, founder and CEO of software provider CodeSee, highlights the importance of chain-of-thought prompting to ensure precise verb selection and prompt refinement. Prompt engineering is emerging as a significant discipline for this reason.

AI’s Coding Strengths:

Developers shared various use cases where AI tools have proven valuable in their work. Some notable examples include:

  1. Solutions to solved problems: Programming often involves reinventing the wheel, which can be frustrating. Jeff Wills, engineering practice lead at Rise8, views this as a prime area for AI assistance. For instance, when Wills creates a method to calculate the distance between two points on a sphere, Copilot can automatically find and generate the Haversine algorithm, eliminating the need for redundant coding.
  2. Updating or cleaning up code: Chris Love of Love2Dev finds ChatGPT particularly useful for updating his existing code. He describes scenarios where he converts older promise-based functions to use the cleaner syntax of async/await or adopts modern syntaxes like destructuring and updated variable declarations.
  3. Potentially faster coding: Many developers perceive that working with Copilot or ChatGPT enables them to work more efficiently, although quantifying the exact impact is challenging. Love believes these tools help him write better code a little faster, while Wills feels they enable him to iterate through potential solutions more swiftly, which enhances the quality of his work.

Conclusion: Generative AI is revolutionizing coding practices by empowering developers with tools like ChatGPT and GitHub Copilot. Programmers are benefitting from AI assistance in solving problems, updating code, and potentially working more efficiently. However, while AI enhances productivity and problem-solving, it is not poised to replace human coders in the near future. The ongoing integration of AI into the software industry promises to reshape workflows and augment the capabilities of human developers, leading to a new era of coding possibilities.

Customer Engagement and Retention

Revolutionizing Customer Engagement and Retention: The Impact of New AI Tools

In today’s highly competitive business landscape, customer engagement and retention have become critical factors for success. Enterprises are constantly exploring innovative ways to attract and retain customers, and one technology that has gained significant attention is artificial intelligence (AI). The advent of new AI tools has revolutionized the way businesses interact with customers, providing unprecedented opportunities for personalized experiences and enhanced customer satisfaction. In this article, we will explore how these new AI tools are transforming customer engagement and retention strategies.

Personalized Customer Experiences:

AI tools have the capability to analyze vast amounts of customer data, including preferences, behaviors, and purchase history, allowing businesses to create highly personalized experiences. By leveraging machine learning algorithms, AI can identify patterns and trends, enabling companies to deliver tailored recommendations, offers, and content. This level of personalization enhances customer engagement, as individuals feel understood and valued by the brand, leading to increased customer satisfaction and loyalty.

Intelligent Chatbots and Virtual Assistants:

AI-powered chatbots eg: ChatGPT and virtual assistants have significantly improved customer engagement and retention by providing quick and efficient support. These tools can understand and respond to customer queries in real-time, offering personalized assistance and resolving issues promptly. Advanced natural language processing capabilities enable chatbots to handle complex interactions, simulating human-like conversations. By reducing response times and improving the overall customer service experience, AI-driven chatbots contribute to higher customer satisfaction and retention rates.

Predictive Analytics for Customer Behavior:

AI tools equipped with predictive analytics capabilities can forecast customer behavior, allowing businesses to anticipate their needs and preferences. By analyzing historical data, AI algorithms can identify potential churn risks, enabling proactive retention strategies. Companies can leverage this knowledge to implement targeted marketing campaigns, loyalty programs, or personalized offers, tailored to individual customers. The ability to predict customer behavior empowers businesses to take proactive measures, resulting in improved customer engagement and increased retention rates.

Sentiment Analysis and Social Listening:

With the proliferation of social media and online review platforms, businesses must stay attuned to customer sentiment. AI tools equipped with sentiment analysis and social listening capabilities can monitor and analyze customer feedback in real-time. By gauging customer emotions and opinions, companies can swiftly address concerns or negative experiences, demonstrating their commitment to customer satisfaction. This proactive approach not only improves customer engagement but also helps in retaining dissatisfied customers by promptly resolving their issues.

Automated Customer Journey Optimization:

AI tools can optimize the customer journey by automating various touchpoints and streamlining processes. Through machine learning algorithms, businesses can analyze customer interactions across multiple channels and identify bottlenecks or areas for improvement. By automating repetitive tasks, such as order processing or appointment scheduling, AI tools free up resources for more meaningful customer engagement. This streamlined and efficient customer journey contributes to better customer experiences, ultimately increasing customer loyalty and retention.

Conclusion: The emergence of new AI tools has revolutionized customer engagement and retention strategies, offering businesses unprecedented opportunities to provide personalized experiences, improve customer service, and optimize the customer journey. By leveraging AI-powered technologies such as personalized recommendations, intelligent chatbots, predictive analytics, sentiment analysis, and automated customer journey optimization, enterprises can enhance customer satisfaction, increase loyalty, and drive long-term growth. As AI continues to evolve, businesses that embrace these transformative tools will gain a competitive edge in the ever-changing customer-centric marketplace.

AI Impact

AI’s Impact on Cost Savings, Productivity, and Jobs: A Revolutionary Force

Artificial Intelligence (AI) has emerged as a revolutionary force that is transforming various aspects of our lives, including cost savings, productivity, and jobs. With its ability to process and analyze vast amounts of data at unprecedented speed, AI is reshaping industries and driving innovation in ways that were previously unimaginable. This article explores the profound impact of AI on cost savings, productivity, and jobs, delving into both the benefits and challenges it presents.

Cost Savings:

One of the most significant impacts of AI is its ability to generate substantial cost savings for businesses across different sectors. By automating routine and repetitive tasks, AI technology enables companies to streamline their operations, reduce labor costs, and optimize resource allocation. AI-powered systems can perform tasks with greater efficiency, accuracy, and consistency compared to humans, leading to improved cost-effectiveness.

AI-powered solutions also enhance cost savings through predictive analytics. By analyzing vast amounts of data, AI algorithms can identify patterns, detect anomalies, and make data-driven predictions. This enables businesses to optimize their supply chain management, inventory control, and production processes, minimizing waste and maximizing efficiency. As a result, companies can achieve significant cost reductions while maintaining or improving the quality of their products and services.

Productivity Boost:

AI is a game-changer when it comes to boosting productivity. By automating mundane and time-consuming tasks, AI frees up human resources to focus on higher-value activities that require creativity, critical thinking, and problem-solving skills. This not only enhances individual productivity but also leads to overall organizational efficiency.

AI-powered tools and systems can handle data analysis, customer support, document processing, and other repetitive tasks with exceptional speed and accuracy. This enables employees to shift their attention to more complex and strategic endeavors that contribute to innovation and growth. By augmenting human capabilities, AI empowers workers to achieve more in less time, accelerating productivity gains across industries.

Job Transformations:

While AI brings numerous benefits, concerns about job displacement have been raised. It is true that AI technologies can automate certain tasks previously performed by humans, leading to workforce restructuring and job transformations. However, history has shown that technological advancements often create new job opportunities that were not previously envisioned.

AI’s impact on jobs can be categorized into three main scenarios: job replacement, job enhancement, and job creation. Some routine and repetitive tasks may be automated, leading to the displacement of certain job roles. However, this also paves the way for job enhancement, as employees can focus on higher-value tasks that require human ingenuity and emotional intelligence. Additionally, AI-driven innovations frequently create new job roles that involve developing, implementing, and maintaining AI systems.

Furthermore, AI is driving the emergence of entirely new industries and business models. The demand for AI specialists, data scientists, and AI consultants has soared, offering new employment opportunities. Moreover, as AI technology continues to advance, it will create a ripple effect, leading to the development of new products, services, and industries, thus generating additional jobs.

Conclusion:

Artificial Intelligence is transforming the business landscape, revolutionizing cost savings, productivity, and jobs. The ability of AI to automate repetitive tasks, optimize processes, and provide data-driven insights offers immense cost savings for businesses across various sectors. Additionally, by augmenting human capabilities and streamlining operations, AI significantly boosts productivity.

While concerns about job displacement exist, AI also presents opportunities for job enhancement and creation. Rather than replacing humans, AI technology empowers workers to focus on higher-value tasks that require uniquely human skills. Moreover, the rise of AI has created new job roles and opened up entirely new industries.

To fully harness the potential of AI, it is crucial for businesses, governments, and individuals to adapt and embrace this transformative technology. By leveraging AI’s capabilities effectively, we can unlock unprecedented cost savings, achieve new levels of productivity, and create a future where humans and AI work in tandem to drive innovation and prosperity.

General availability of GPT-4

OpenAI Announces General Availability of GPT-4

OpenAI has made an exciting announcement today, revealing the general availability of GPT-4, its latest text-generation model, via the OpenAI API. Existing OpenAI API developers with a successful payment history can access GPT-4 starting immediately. OpenAI plans to gradually expand access to new developers by the end of this month, and availability limits will be adjusted based on compute availability.

Since March, millions of developers have eagerly requested access to the GPT-4 API, and OpenAI is witnessing a growing range of innovative products leveraging the power of GPT-4. OpenAI envisions a future where chat-based models can support any use case.

Compared to its predecessor, GPT-3.5, GPT-4 offers notable improvements. It can generate text, including code, and accepts both image and text inputs. GPT-4 performs at a “human level” on various professional and academic benchmarks. The training data for GPT-4 includes publicly available information from web pages, as well as licensed data obtained by OpenAI.

However, the image-understanding capability of GPT-4 is currently limited to a single partner, Be My Eyes, as OpenAI conducts testing. OpenAI has not disclosed when this functionality will be made available to a wider customer base.

It’s important to note that, like any generative AI model, GPT-4 is not perfect. It may occasionally “hallucinate” facts and make reasoning errors, sometimes with unwarranted confidence. It also lacks the ability to learn from experience, making it prone to failures in complex tasks such as introducing security vulnerabilities in generated code.

OpenAI plans to introduce the capability for developers to fine-tune GPT-4 and GPT-3.5 Turbo, another recent text-generation model, with their own data, as has been possible with previous OpenAI models. This feature is expected to be available later this year.

The competition in generative AI has been intensifying since the unveiling of GPT-4 in March. Anthropic, for instance, expanded the context window of its text-generating model Claude from 9,000 tokens to 100,000 tokens. Context window refers to the amount of text considered by the model before generating additional text, while tokens represent units of raw text. GPT-4 previously held the record for a context window, offering up to 32,000 tokens. Models with smaller context windows tend to forget the content of recent conversations, leading to a loss of coherence.

In a related announcement, OpenAI also made its DALL-E 2 image-generating model and Whisper speech-to-text model APIs generally available. OpenAI plans to deprecate older models in its API to optimize compute capacity. Starting from January 4, 2024, GPT-3 and its derivatives will no longer be accessible. They will be replaced by new “base GPT-3” models, which are expected to be more computationally efficient. Developers currently using the older models will need to manually upgrade their integrations by January 4. Those interested in continuing to use fine-tuned old models beyond that date will have to fine-tune replacements based on the new base GPT-3 models. OpenAI will provide support to users to ensure a smooth transition and will reach out to developers who have recently used the older models with further instructions and information about testing the new completio