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GPT-4 AI Chatbot Scores High on Tests

GPT-4, the latest version of the artificial intelligence chatbot ChatGPT, has achieved impressive scores on a range of high school and law school tests, according to its creator OpenAI. The new version of the chatbot has demonstrated improved processing capabilities, including the ability to convert image, audio, and video inputs to text, and handle more nuanced instructions creatively and reliably.

The most notable achievement of GPT-4 is its performance on the LSAT, the test that college students in the United States must pass to be admitted to law school. GPT-4 scored 163, which puts it in the 88th percentile and in a good position to be admitted to a top 20 law school. The score is only a few marks short of the reported scores needed for acceptance to prestigious schools like Harvard, Stanford, Princeton, and Yale. The prior version of ChatGPT only scored 149 on the LSAT, putting it in the bottom 40%.

GPT-4 also excelled on the Uniform Bar Exam, which is taken by recently graduated law students to practice as a lawyer in any U.S. jurisdiction. GPT-4 scored 298 out of 400, while the old version of ChatGPT scored only 213 out of 400.

In addition to law school exams, GPT-4 also achieved high scores on the SAT Evidence-Based Reading & Writing and SAT Math exams, scoring in the 93rd and 89th percentile, respectively. It also performed well on AP exams in biology, chemistry, and physics, with scores ranging from 66-100%. However, its AP Calculus score was fairly average, ranking in the 43rd to 59th percentile.

Despite its strengths, GPT-4 struggled in English literature exams, scoring in the 8th to 44th percentile across two separate tests.

Overall, the test results demonstrate that GPT-4 has made significant advancements compared to its prior version, with improved processing capabilities and the ability to pass high school and law school tests with scores ranking in the 90th percentile. These developments are significant for the field of artificial intelligence and have implications for the use of chatbots and similar technologies in various industries, including education and legal services.

Tech leaders sign open letter calling for AI development halt

Over 2,600 tech industry leaders and researchers, including Tesla CEO Elon Musk and Apple co-founder Steve Wozniak, have signed an open letter calling for a temporary halt on any further artificial intelligence (AI) development. The letter expresses concerns about the potential hazards to society and mankind posed by AI with human-competitive intelligence, citing the risks of AI systems that may be able to learn and evolve beyond human control.

The signatories of the letter urge all AI firms to immediately cease developing AI systems that are more potent than Generative Pre-trained Transformer 4 (GPT-4) for at least six months. GPT-4 is a multimodal large language model created by OpenAI and the fourth in its GPT series. The aim of the proposed moratorium is to allow time for comprehensive risk assessments to be carried out and for the development of new safety protocols.

However, the petition has divided the tech community, with some opposing the call to halt AI development. Coinbase CEO Brian Armstrong, among other notable names, voiced his opposition to the petition, stating that “committees and bureaucracy won’t solve anything.” Armstrong added that there are no designated “experts” to decide on this issue and that not everyone in the tech industry agrees with the petition.

Armstrong argued that the risks of new technologies, including AI, are an inherent part of progress, and that centralization in decision-making will bring no good. He reminded that any new technology poses a certain amount of danger, but the goal should be to keep moving forward.

A columnist at LA Times, Brian Merchant, called the petition an “apocalyptic AI hype carnival” and stated that many of the stated concerns are “robot jobs apocalypse” stuff. Meanwhile, Satvik Sethi, a former Web3 executive at Mastercard, described the petition as a “non-proliferation treaty but for AI.” He added that many of the popular signers on the list have a deeply personal vested interest in the AI field and are likely just “trying to slow down their counterparts so they can get ahead.”

The debate around the open letter highlights the complex and multifaceted challenges of AI development. While some experts view the potential benefits of AI as significant, there are also concerns about the potential risks to society and mankind. The debate highlights the need for continued discussion and collaboration among all stakeholders to ensure that the development of AI is safe, ethical, and aligned with the long-term interests of humanity.

GitHub Copilot X: The Future of AI-Powered Software Development

GitHub Copilot X is an advanced version of GitHub’s AI pair programmer, designed to integrate into every part of a developer’s workflow. The platform is a vision for the future of AI-powered software development, featuring chat and terminal interfaces, support for pull requests, and early adoption of OpenAI’s GPT-4.

Key Features

1. Context-Aware Conversations

GitHub Copilot X is equipped with the ability to offer context-aware conversations. This feature empowers developers to ask the AI to elucidate a piece of code or rectify an error. Moreover, it can generate unit tests, thereby enabling developers to concentrate on building their projects.

2. Personalized Documentation

GitHub Copilot X provides personalized answers that are firmly grounded in maintainer-written documentation. This feature significantly reduces the time developers spend searching for information. The process is straightforward: load content, pose a question, and receive the answer.

3. Pull Requests

GitHub Copilot X meticulously tracks a developer’s work, suggests descriptions for pull requests, and aids reviewers in understanding changes through a code walkthrough. Additionally, it offers AI-generated PR descriptions and can identify missing unit tests and generate new test cases after every build.

4. Command Line Interface (CLI) Assistance

GitHub Copilot X can offer assistance directly in the terminal. If a developer forgets how to delete a tag or requires help with multi-step shell commands and scripting, they can ask GitHub Copilot for assistance.

Availability and Pricing

Currently, GitHub Copilot X serves as a representation of GitHub’s vision for the future rather than an available product offering. The company is actively engaged in designing, testing, and building features that align with the GitHub Copilot X vision. The pricing and availability of these features are yet to be determined.

Access Prerequisites

Access to the technical preview features of GitHub Copilot X is not guaranteed by an active or trial subscription to GitHub Copilot for Individuals or GitHub Copilot for Business. Interested users can join the waitlist to preview the features they are interested in. Once access is granted, users should bear in mind that the feature is considered a beta or technical preview, implying it may still have some kinks to be ironed out.

Responsible Use of AI

GitHub is committed to the responsible use of AI. The company applies sentiment analysis to suggestions to prevent the inclusion of slang, slurs, and hate speech in GitHub Copilot responses. It also evaluates the quality of every suggestion and nudges developers toward better quality code. User data is safeguarded with measures such as data encryption both in transit and at rest.

Impact on Developers

Research indicates that GitHub Copilot aids developers in coding faster, staying in the flow longer, and feeling more fulfilled with their work. According to the data provided, 74% of users can focus on more satisfying work, 88% feel more productive, and 96% are faster with repetitive tasks.

The advent of GitHub Copilot X, with its advanced AI capabilities, has sparked discussions about the future role of developers and programmers. This tool can automate repetitive tasks, provide insights, and assist in debugging code, potentially reducing the demand for junior programmers who often handle such tasks.

However, it’s crucial to note that while GitHub Copilot X and similar tools are designed to augment the work of developers, they are not intended to replace them entirely. Despite the automation of certain tasks, these tools currently cannot replicate the unique human abilities such as creativity, problem-solving skills, and strategic thinking that are integral to software development.

The goal of GitHub Copilot X is to make coding more efficient and accessible, thereby enabling developers to focus on higher-level tasks and innovative solutions. In this light, rather than replacing developers, GitHub Copilot X is set to become a valuable tool in their arsenal, enhancing productivity and the quality of work. It may also shift the focus of programming roles, emphasizing more on strategic and creative aspects of software development.

Future Vision

The “X” in GitHub Copilot X represents a placeholder for where GitHub intends Copilot to become available and what it expects it to be capable of doing. It signifies the product’s extension from one experience, code completion, to multiple experiences across the developer’s workflow. The “X” also indicates the magnitude of impact GitHub intends to have on developer achievement. It’s a statement of intent and a commitment to developers as the industry enters the age of AI.

What is Agent GPT?

Agent GPT is an innovative autonomous AI Agent platform that empowers users to create and deploy customizable autonomous AI agents directly on the web. This comprehensive overview explores the features, functionalities, use cases, and future prospects of Agent GPT.

Introduction

Agent GPT is an open-source project that allows users to create autonomous AI agents built on GPT-4. These agents can act autonomously, write their own code, and perform various tasks on the internet. The platform is gaining popularity for its user-friendly interface, customization options, and potential applications ranging from chatbots to workflow automation.

Features and Functionality

1. Open-Source and Customizable

Agent GPT is open-source, allowing developers to contribute and customize according to their needs. Users can create custom chatbots and assign a name and goal to their AI agent.

2. Built on GPT-4

Agent GPT leverages GPT-4, enabling it to act autonomously, write its own code, and even debug and develop itself.

3. No-Code Solution

With its browser-based, no-code solution, Agent GPT makes AI accessible to a broader audience without requiring extensive programming knowledge.

4. Versatile Applications

Beyond chatbots, Agent GPT can be used for automation, Discord bots, Auto-GPT apps, and more.

5. Compatibility

Agent GPT is a browser-based tool that can be run locally using Docker or Nodejs, making it compatible with modern web browsers and various settings.

Use Cases of Agent GPT

Agent GPT’s versatile and dynamic nature allows for a wide array of applications.

Code Assistance: Debugging code, generating code snippets, providing coding tutorials.

Research and Content Generation: Crafting blog posts, writing articles, compiling study guides, and summaries.

Email and Communication: Automating email writing, drafting messages, and other communication forms.

Marketing and Advertising: Generating marketing ideas, creating ad copy, and assisting with SEO strategies.

Budgeting and Financial Planning: Providing budgeting advice, financial management tips, and creating personal financial plans.

Limitations of Agent GPT

Agent GPT, while a powerful and versatile platform, has certain limitations that users should be aware of. As of the current version, Agent GPT does not have the ability to generate outputs in certain ways, although this feature is actively being developed. There are restrictions on how much the Agent can run due to limitations on API usage and associated infrastructure costs. Users may encounter caps on usage, although options like hosting AgentGPT locally or subscribing to a pro plan can circumvent these limitations.

At present, each Agent run is independent, and resuming a previous run is not possible, although this functionality is planned for future updates. Free-tier users of AgentGPT utilize GPT-3.5, while PRO users have access to GPT-4, which may affect the capabilities and performance of the agent. Additionally, the output length is limited to manage generation costs on the platform’s end, and while adjustments can be made within advanced settings, this may still restrict the extent of the generated content.

These limitations are part of the ongoing development and refinement of the platform, and users are encouraged to consult the roadmap and official documentation for updates and future enhancements.

Future Prospects

The entire AgentGPT team is excited about the road ahead, with many exciting features planned for the future. Users are encouraged to follow the roadmap to stay updated on upcoming developments.

Conclusion

Agent GPT is a novel tool that is revolutionizing the way we interact with AI. Its open-source nature, user-friendly interface, versatile applications, and continuous development make it a valuable asset for developers, businesses, and individuals alike. As research continues to unfold, Agent GPT’s potential is likely to expand, offering new possibilities for automation, customization, and intelligent decision-making.

World's Largest Law Firm Dentons to Launch fleetAI, Proprietary Version of ChatGPT

Dentons, the world’s largest global law firm, has announced plans to launch a proprietary version of ChatGPT, named “fleetAI,” that will enable its lawyers to apply generative artificial intelligence (AI) to active client matters. The announcement was made in London and the tool is set to launch in August 2023.

The system includes a chatbot based on OpenAI’s GPT-4 Large Language Model, allowing lawyers to conduct legal research, generate legal content, and identify relevant legal arguments. A second bot within fleetAI will enable the uploading of multiple legal documents for key data extraction, including clauses and obligations, for analysis and querying.

Dentons has collaborated with Microsoft to ensure that all data uploaded into fleetAI is not used to train the model, cannot be accessed by anyone outside of Dentons, and is erased after 30 days. Following the August 2023 launch, there will be a 6-week beta testing period, after which practice group leaders will review feedback and produce practice-specific usage guidance.

Future versions of fleetAI are already in development, including integration with Dentons’ existing legal robots that automate data extraction from Companies House and analyze clients’ employment tribunal claims to predict future outcomes. Other instances under development include a knowledge chatbot and a Business Services chatbot for internal policies.

Paul Jarvis, UK, Ireland, and Middle East CEO of Dentons, emphasized the transformative potential of the tool, stating, “The ability to upload and analyse client matter documents at speed and in a secure manner is the real game-changer – we believe Dentons will be the first law firm that has the technology to systematically incorporate generative AI into our day-to-day matter workflows.” He further added, “The use cases for fleetAI have been identified and tested with clients during the development phase and we are confident this is going to fundamentally transform the way we deliver services to them.”

The launch of fleetAI represents a significant step in the integration of AI into the legal industry, particularly in the area of document analysis and legal research. With the collaboration of Microsoft and a focus on security and client-specific needs, Dentons is positioning itself at the forefront of technological innovation within the legal sector. The firm’s commitment to a portfolio approach, including the trial of third-party products, further underscores its dedication to leveraging technology to enhance legal services.

OpenAI Explores GPT-4 for Content Moderation

OpenAI, a pioneering organization in the area of artificial intelligence, is now investigating the role that Large Language Models (LLMs) such as GPT-4 may play in the process of content moderation. The major goal is to optimize and improve the content moderation process by using the skills of these models to comprehend natural language and generate that language, which will speed the process.

According to a recent post on OpenAI’s official blog, the application of GPT-4 in content moderation can significantly reduce the time taken to develop and customize content policies. Traditionally, this process could span months, but with GPT-4, it can be condensed to mere hours. Here’s a brief overview of the process:

Policy Guideline Creation: Initially, a policy guideline is formulated. Following this, policy experts curate a “golden set” of data, marking specific examples and labeling them in accordance with the policy.

GPT-4’s Role: After then, GPT-4 conducts an independent assessment of the policy and assigns labels to the dataset without having previous knowledge of the responses that were supplied by experts.

Iterative Refinement: Any inconsistencies between the evaluations provided by GPT-4 and those of human specialists are carefully examined. This entails GPT-4 elaborating on its rationale for certain labels, which makes it possible for specialists to identify areas of ambiguity in policy definitions. After that, the policy may be defined and improved upon. This cycle, consisting of phases 2 and 3, is continued until the quality of the policy is deemed to be adequate.

The development of more nuanced content restrictions represents the successful completion of this iterative process. After that, these rules may be converted into classifiers, which will make it much easier to put the policy into action and will make it possible to moderate material on a much larger scale. In addition, the predictions made by GPT-4 may be leveraged to fine-tune smaller models, so guaranteeing that efficiency is maintained even when dealing with huge volumes of data.

In conclusion, the research of GPT-4 for the purpose of content moderation that OpenAI has been doing provides a potential route for improving the efficacy and accuracy of the content moderation procedures.

OpenAI Announces Call for Experts to Join its Red Teaming Network

OpenAI has initiated an open call for its Red Teaming Network, seeking domain experts to enhance the safety measures of its AI models. The organization aims to collaborate with professionals from diverse fields to meticulously evaluate and “red team” its AI systems.

Understanding the OpenAI Red Teaming Network

The term “red teaming” encompasses a wide array of risk assessment techniques for AI systems. These methods range from qualitative capability discovery to stress testing and providing feedback on the risk scale of specific vulnerabilities. OpenAI has clarified its use of the term “red team” to avoid confusion and ensure alignment with the language used with its collaborators.

Over the past years, OpenAI’s red teaming initiatives have evolved from internal adversarial testing to collaborating with external experts. These experts assist in developing domain-specific risk taxonomies and evaluating potential harmful capabilities in new systems. Notable models that underwent such evaluation include DALL·E 2 and GPT-4.

The newly launched OpenAI Red Teaming Network aims to establish a community of trusted experts. These experts will provide insights into risk assessment and mitigation on a broader scale, rather than sporadic engagements before significant model releases. Members will be selected based on their expertise and will contribute varying amounts of time, potentially as little as 5-10 hours annually.

Benefits of Joining the Network

By joining the network, experts will have the opportunity to influence the development of safer AI technologies and policies. They will play a crucial role in evaluating OpenAI’s models and systems throughout their deployment phases.

OpenAI emphasizes the importance of diverse expertise in assessing AI systems. The organization is actively seeking applications from experts worldwide, prioritizing both geographic and domain diversity. Some of the domains of interest include Cognitive Science, Computer Science, Political Science, Healthcare, Cybersecurity, and many more. Familiarity with AI systems is not a prerequisite, but a proactive approach and unique perspective on AI impact assessment are highly valued.

Compensation and Confidentiality

Participants in the OpenAI Red Teaming Network will receive compensation for their contributions to red teaming projects. However, they should be aware that involvement in such projects might be subject to Non-Disclosure Agreements (NDAs) or remain confidential for an indefinite duration.

Application Process

Those interested in joining the mission to develop safe AGI for the benefit of humanity can apply to be a part of the OpenAI Red Teaming Network.

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Microsoft Unveils Free Copilot AI App for Android, Featuring GPT-4 and Challenging Paid Alternatives

Microsoft has launched its Copilot AI app, a comprehensive generative AI solution, for Android users. This free application, formerly known as Bing Chat, integrates the advanced GPT-4 AI model, offering a range of capabilities, including text generation and image creation with DALL-E 3. This release marks a notable step in Microsoft’s AI endeavors, positioning Copilot as a strong competitor against paid alternatives and setting the stage for an anticipated iOS version.

Microsoft Copilot’s standalone app on the Google Play Store represents a shift from its integration within the Bing search engine, offering users direct access to its features. The app includes functionalities akin to the ChatGPT app, enabling users to perform various tasks such as image generation, email drafting, songwriting, and more. Its wide array of features and user-friendly interface make it a versatile tool for Android users.

A key advantage of Microsoft Copilot is its free access to GPT-4, contrasting with the official GPT 4 chatbot by ChatGPT, which requires a subscription for ChatGPT Plus. Free users of ChatGPT are limited to GPT 3.5, missing out on the advanced features available in Copilot. This difference highlights Microsoft’s strategy to leverage its AI technology to attract a broader user base by offering advanced capabilities without cost.

While Copilot and ChatGPT share certain functionalities, they serve distinct purposes and user needs. ChatGPT, available in free, Plus, and Enterprise versions, caters to a wide range of content creation tasks, including essays, emails, and code generation. It is designed for a generalized audience, with capabilities extending to multi-modal interactions and content exportation. ChatGPT Plus offers enhanced features such as internet browsing, visual prompt responses, and advanced context retention.

On the other hand, Microsoft Copilot, integrated into the Microsoft ecosystem, is tailored to enhance productivity and efficiency within Microsoft 365 applications. It assists users in tasks like content development in Word, email management in Outlook, presentation creation in PowerPoint, data analysis in Excel, and team collaboration in Microsoft Teams. Copilot’s specific focus on the Microsoft ecosystem makes it an ideal tool for users seeking to augment their experience with Microsoft applications.

Microsoft’s Copilot AI app for Android, featuring GPT-4, offers a free, powerful alternative to paid AI tools. Its integration with the Microsoft ecosystem and broad capabilities provide users with a robust tool for enhancing productivity and creativity. The release of Copilot on Android sets a new benchmark in the AI application landscape and foreshadows further advancements with an iOS version on the horizon.

Here's Why GPT-4 Becomes 'Stupid': Unpacking Performance Degradation

The realm of artificial intelligence (AI) and machine learning (ML) is constantly advancing, yet it’s not without its stumbling blocks. A prime example is the performance degradation, colloquially referred to as ‘stupidity’, in Large Language Models (LLMs) like GPT-4. This issue has gained traction in AI discussions, particularly following the publication of “Task Contamination: Language Models May Not Be Few-Shot Anymore,” which sheds light on the limitations and challenges faced by current LLMs.

Chomba Bupe, a prominent figure in the AI community, has highlighted on X (formerly Twitter) a significant issue: LLMs tend to excel in tasks and datasets they were trained on but falter with newer, unseen data. The crux of the problem lies in the static nature of these models’ post-training. Once their learning phase is complete, their ability to adapt to new and evolving input distributions is restricted, leading to a gradual decline in performance.

Source: DALL·E Generation

This degradation is especially concerning in domains like programming, where language models are employed and where updates to programming languages are frequent. Bupe points out that the fundamental design of LLMs is more about memorization than understanding, which limits their effectiveness in tackling new challenges.

The research conducted by Changmao Li and Jeffrey Flanigan further supports this viewpoint. They found that LLMs like GPT-3 demonstrate superior performance on datasets that predate their training data. This discovery indicates a phenomenon known as task contamination, where the models’ zero-shot and few-shot capabilities are compromised by their training data’s limitations.

Continual learning, as discussed by Bupe, emerges as a key area in machine intelligence. The challenge is developing ML models that can adapt to new information without compromising their performance on previously learned tasks. This difficulty is contrasted with the adaptability of biological neural networks, which manage to learn and adapt without similar drawbacks.

Alvin De Cruz offers an alternate perspective, suggesting the issue might lie in the evolving expectations from humans rather than the models’ inherent limitations. However, Bupe counters this by emphasizing the long-standing nature of these challenges in AI, particularly in the realm of continual learning.

To sum up, the conversation surrounding LLMs like GPT-4 highlights a critical facet of AI evolution: the imperative for models capable of continuous learning and adaptation. Despite their impressive abilities, current LLMs face significant limitations in keeping pace with the rapidly changing world, underscoring the need for more dynamic and evolving AI solutions.

Gemini Pro vs GPT-4: A Comprehensive Comparison of AI Powerhouses

The world of artificial intelligence (AI) is witnessing a significant rivalry with Google’s Gemini Pro and OpenAI’s GPT-4 at the forefront. These advanced multimodal AI models are pushing the boundaries in various domains, including reasoning, math, language understanding, and coding skills. Recently, a research paper titled “Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models” delves into a detailed comparison of these two AI titans, highlighting their unique capabilities and performance benchmarks.

Performance Analysis

Gemini Pro, announced by Google on December 6, 2023, represents the pinnacle of Google’s AI development. It’s not just a language model but a versatile multimodal AI capable of handling text, image, video, and audio data. In comparison to GPT-4, Gemini Pro has demonstrated superior performance in reasoning and math benchmarks, and has shown higher efficiency in code generation and problem-solving tasks.

Data Sets and Experiments

A recent study by researchers from Stanford and Meta evaluated the performance of Gemini Pro, GPT-3.5 Turbo, and GPT-4 Turbo across 12 commonsense reasoning datasets, encompassing general, professional, and social reasoning, as well as multimodal datasets. Gemini Pro’s overall performance was found to be comparable to GPT-3.5 Turbo and slightly behind GPT-4 Turbo.

Real-World Applications

The practical applications of Gemini Pro are extensive. It powers Google Bard and is available to developers and organizations via the Gemini API and Google Cloud’s Vertex AI platform. The model’s free access through AI Studio allows developers to experiment and integrate its capabilities into various applications.

Google has recently introduced a suite of generative AI tools, including Imagen 2 and Duet AI, alongside the Gemini API. Imagen 2, an advanced text-to-image diffusion technology, and MedLM, a foundation model fine-tuned for the healthcare industry, represent Google’s commitment to expanding the applications of AI in different fields. Duet AI, available for developers and security operations, further extends the potential use cases of AI in application development and cybersecurity.

Conclusion

The comparison between Google’s Gemini Pro and OpenAI’s GPT-4 highlights the rapid advancement in AI capabilities. While GPT-4 leads in commonsense reasoning tasks, Gemini Pro excels in reasoning, math, and multimodal tasks. This competition is driving innovation and broadening the scope of AI applications across various industries.