Category: Prompt-AI-1

A recent study conducted by researchers from Stanford University concludes that current large language models (LLMs) such as OpenAI’s GPT-4 and Google’s Bard are failing to meet the compliance standards set by the European Union (EU) Artificial Intelligence (AI) Act.

Understanding the EU AI Act

The EU AI Act, the first of its kind to regulate AI on a national and regional scale, was recently adopted by the European Parliament.

It not only oversees AI within the EU, a region housing 450 million people but also sets the precedent for AI regulations globally.

However, as per the Stanford study, AI companies have a considerable distance to cover to attain compliance.

Compliance Analysis of AI Providers

In their study, the researchers evaluated ten major model providers against the 12 requirements of the AI Act, scoring each provider on a 0 to 4 scale.

Stanford’s report says:

“We present the final scores in the above figure with the justification for every grade made available. Our results demonstrate a striking range in compliance across model providers: some providers score less than 25% (AI21 Labs, Aleph Alpha, Anthropic) and only one provider scores at least 75% (Hugging Face/BigScience) at present. Even for the highest-scoring providers, there is still significant margin for improvement. This confirms that the Act (if enacted, obeyed, and enforced) would yield significant change to the ecosystem, making substantial progress towards more transparency and accountability.”

The findings displayed a significant variation in compliance levels, with some providers scoring below 25%, and only Hugging Face/BigScience scoring above 75%.

This suggests a considerable scope for improvement even for high-scoring providers.

The Problem Areas

The researchers highlighted key areas of non-compliance, including a lack of transparency in disclosing the status of copyrighted training data, energy consumption, emissions, and risk mitigation methodology.

They also observed a clear difference between open and closed model releases, with open releases providing better disclosure of resources but posing bigger challenges in controlling deployment.

The study concludes that all providers, regardless of their release strategy, have room for improvements.

A Reduction in Transparency

In recent times, major model releases have seen a decline in transparency.

OpenAI, for instance, chose not to disclose any data and compute details in their reports for GPT-4, citing competitive landscape and safety implications.

Potential Impact of the EU AI Regulations

The Stanford researchers believe that the enforcement of the EU AI Act could significantly influence the AI industry.

The Act emphasises the need for transparency and accountability, encouraging large foundation model providers to adapt to new standards.

However, the swift adaptation and evolution of business practices to meet regulatory requirements remain a major challenge for AI providers.

Despite this, the researchers suggest that with robust regulatory pressure, providers could achieve higher compliance scores through meaningful yet feasible changes.

The Future of AI Regulation

The study offers an insightful perspective on the future of AI regulation.

The researchers assert that if properly enforced, the AI Act could substantially impact the AI ecosystem, promoting transparency and accountability.

As we stand on the threshold of regulating this transformative technology, the study emphasises the importance of transparency as a fundamental requirement for responsible AI deployment.

AI chatbot, ChatGPT, a creation of OpenAI, is under scrutiny due to its frequent inability to distinguish fact from fiction, leaving users often led astray by the information it provides.

The Warning Sign Often Ignored

OpenAI has highlighted on its homepage one of the many limitations of ChatGPT – it may sometimes provide incorrect information.

Although this warning holds true for several information sources, it brings to light a concerning trend. Users often disregard this caveat, assuming the data provided by ChatGPT to be factual.

Unreliable Legal Aid, The Case of Steven A. Schwartz

The misleading nature of ChatGPT came into stark focus when US lawyer Steven A.

Schwartz turned to the chatbot for case references in a lawsuit against Colombian airline Avianca. In a turn of events, all the cases the AI suggested turned out to be non-existent.

Despite Schwartz’s concerns about the veracity of the information, the AI reassured him of its authenticity.

Such instances raise questions about the chatbot’s reliability.

A Misunderstood Reliable Source?

The frequency with which users treat ChatGPT as a credible source of information calls for a wider recognition of its limitations.

Over the past few months, there have been several reports of people being misled by its fallacies, which have been largely inconsequential but nonetheless worrying.

One concerning instance involved a Texas A&M professor who used ChatGPT to verify if students’ essays were AI-generated.

ChatGPT confirmed, incorrectly, that they were, leading to the threat of failing the entire class. This incident underscores the risk of the misinformation that ChatGPT can spread, potentially leading to more serious consequences.

Cases like these do not entirely discredit the potential of ChatGPT and other AI chatbots. In fact, these tools, under the right conditions and with adequate safeguards, could be exceptionally useful.

However, it’s crucial to realize that at present, their capabilities are not entirely reliable.

The Role of the Media and OpenAI

The media and OpenAI bear some responsibility for this issue.

Media often portrays these systems as emotionally intelligent entities, failing to emphasize their unreliability. Similarly, OpenAI could do more to warn users of the potential misinformation that ChatGPT can provide.

Recognizing ChatGPT as a Search Engine

The tendency of users to utilize ChatGPT as a search engine should be acknowledged by OpenAI, leading them to provide clear and upfront warnings.

Chatbots present information in a regenerated text format and a friendly, all-knowing tone, making it easy for users to assume the information is accurate.

This pattern reinforces the need for stronger disclaimers and cautionary measures from OpenAI.

The Path Forward

OpenAI needs to implement changes to reduce the likelihood of users being misled.

This could include programming ChatGPT to caution users to verify its sources when asked for factual citations, or making it clear when it is incapable of making a judgment.

OpenAI has indeed made improvements, making ChatGPT more transparent about its limitations.

However, inconsistencies persist and call for more action to ensure that users are fully aware of the potential for error and misinformation.

Without such measures, a simple disclaimer like “May occasionally generate incorrect information” seems significantly inadequate.

Researchers have found a new method to determine whether a piece of text was penned by a human or an artificial intelligence (AI).

This new detection technique leverages a model named RoBERTa, which helps to analyze the structure of text.

Finding the Differences

The study revealed that the text produced by AI systems, such as ChatGPT and Davinci, displays different patterns compared to human text.

When these texts were visualized as points in a multi-dimensional space, it was found that the points representing AI-written text occupied a lesser area than the points representing human-written text.

Using this key difference, researchers designed a tool that can resist common tactics employed to camouflage AI-written text.

The performance of this tool remained impressive even when it was tested with various types of text and AI models, showing high accuracy.

However, its accuracy decreased when the tool was tested with a sophisticated hiding method called DIPPER.

Despite this, it still performed better than other available detectors.

One of the exciting aspects of this tool is its capability to work with languages other than English. The research showed that while the pattern of text points varied across languages, AI-written text consistently occupied a lesser space than human-written text in every specific language.

Looking Ahead

While the researchers acknowledged that the tool faces difficulties when dealing with certain types of AI-generated text, they remain optimistic about potential enhancements in the future.

They also suggested exploring other models, similar to RoBERTa, for understanding the structure of text.

Earlier this year, OpenAI introduced a tool designed to distinguish between human and AI-generated text.

Although this tool provides valuable assistance, it is not flawless and can sometimes misjudge. The developers have made this tool publicly available for free to receive feedback and make necessary improvements.

These developments underscore the ongoing endeavors in the tech world to tackle the challenges posed by AI-generated content. Tools like these are expected to play a crucial role in battling misinformation campaigns and mitigating other harmful effects of AI-generated content.

Polygon, a well-known developer that provides Ethereum scaling solutions, has leaped into the future of Web3. They have introduced an AI chatbot assistant, Polygon Copilot, to their platform.

What is Polygon Copilot?

Imagine a personal guide that can help you navigate the expansive ecosystem of decentralized applications (dApps) on Polygon.

“Where can I find an AI-powered guide to Polygon and web3?” … 📡

Introducing Polygon Copilot, powered by @LayerEhq and @OpenAI GPT-4. AKA, your friendly AI guide trained on all Polygon docs and the web3 universe.

More: https://t.co/GZ1qBx93nq pic.twitter.com/Nu7q13fcIJ

— Polygon (Labs) (@0xPolygonLabs) June 21, 2023

Polygon Copilot is just that! It’s an AI assistant that can answer your questions and provide information about the Polygon platform.

It comes with three different user levels: Beginner, Advanced, and Degen, each designed for users at different stages of familiarity with the ecosystem.

The assistant is built on OpenAI’s GPT-3.5 and GPT-4 models and is incorporated into the user interface of Polygon.

One of the main goals of the Copilot is to offer insights, analytics, and guidance based on the Polygon protocol documentation.

A standout feature of Polygon Copilot is its commitment to transparency. It discloses the sources of the information it gives, which enables users to verify the information and explore the topic further.

Polygon’s step towards integrating AI technology is part of a growing trend in the Web3 world.

Other companies including Alchemy, Solana Labs, and Etherscan are also harnessing the potential of AI.

Using Polygon Copilot

To start with Polygon Copilot, users need to connect a wallet that will serve as the user account.

This account is given credits for asking questions, with new credits added every 24 hours.

And what sets Polygon Copilot apart? It’s not just any plain-speaking AI; it has a flair of its own. Ask it about the top NFT project on Polygon, and you’ll get a response full of personality.

However, it’s essential to remember that like all AI technology, Polygon Copilot isn’t perfect.

Users are cautioned that the AI may provide inaccurate information and to take the chatbot’s answers with a grain of salt.

Polygon has set limits on the number of responses the chatbot can generate to prevent spamming and overload.

What’s Polygon All About?

Polygon presents itself as ‘Ethereum 2.0’, addressing scalability issues within the Ethereum blockchain.

It enhances the value of any applications built on the Ethereum blockchain.

The introduction of the AI assistant is a leap forward for the platform. Whether you are a beginner looking for basic guidance or an advanced user trying to build complex products, Polygon Copilot is there to assist.

It’s also handy for analysts seeking accurate data about NFTs and dApps.

Web3 and the Promise of Data Ownership

Polygon’s use of AI reflects the evolution of the internet, known as Web 3.0. This version of the internet promises safety, transparency, and control over the data created by users.

Web 3.0 operates on blockchain technology, a decentralized system that removes corporate access to private data.

Blockchains were born alongside Bitcoin, the first cryptocurrency, aiming to break free from corporations’ control over our data.

In the spirit of Web 3.0, platforms like Polygon allow users to control access to their data and attach value to it, enhancing data ownership.

As the tech world moves forward, innovations like Polygon Copilot highlight the growing intersection between artificial intelligence and blockchain technology, redefining user experience in the process.

Amazon Web Services (AWS), the cloud computing division of Amazon, has announced a strategic investment of $100 million in a new initiative called the AWS Generative AI Innovation Center.

This move aims to bolster startups and businesses focused on generative artificial intelligence, a rapidly growing field in AI.

The investment underscores AWS’s commitment to staying at the forefront of technological advancements as it competes with industry giants like Microsoft and Google.

Generative AI is a subset of AI that goes beyond traditional classification and prediction algorithms. Instead, it enables the generation of new content, including text, images, and music, based on learned patterns.

This innovative technology has the potential to significantly enhance productivity and creativity by offering novel solutions and ideas.

AWS’s Ongoing Efforts in Generative AI

The AWS Generative AI Innovation Center aims to connect AWS-affiliated experts, including data scientists, strategists, engineers, and solutions architects, with customers and partners to accelerate enterprise innovation in the field of generative AI.

By encouraging collaboration and delivering resources, AWS seeks to empower businesses in leveraging generative AI to drive success and growth.

Sri Elaprolu, heading the AWS Generative AI Innovation Center, highlighted the program’s objectives and its potential impact on various sectors.

Initially, the center will prioritize customers who have demonstrated interest in generative AI, focusing on industries such as financial services, healthcare, life sciences, media and entertainment, automotive, energy, utilities, and telecommunications.

This $100 million investment follows AWS’s recent efforts to promote generative AI, including a 10-week program for generative AI startups and the launch of Bedrock, a platform for building generative AI-powered applications.

Additionally, AWS has been collaborating with Nvidia to develop next-generation infrastructure for training AI models, supplementing its existing Trainium hardware.

The significant economic potential of generative AI is evident, with projections suggesting a potential addition of $4.4 trillion to the global economy annually.

As the AI industry continues to expand, reaching an estimated worth of $15.7 trillion by 2030, AWS’s strategic investment positions them to tap into this immense opportunity.

While challenges remain, such as meeting the demand for AI chips and ensuring enterprise security, AWS remains confident in its ability to deliver customer-centric solutions.

By prioritizing customer needs and leveraging its expertise, AWS aims to solidify its position as a leading generative AI services and support provider.

As the race for dominance in AI intensifies, Amazon’s substantial investment reaffirms its commitment to staying ahead of the curve and driving innovation in the ever-evolving field of generative AI.

Artificial Intelligence (AI) is revolutionizing numerous sectors, but with the boon comes the bane. AI image generators are becoming more sophisticated, making the task of detecting deepfakes increasingly difficult.

This issue is causing alarm among global leaders and law enforcement agencies who are concerned about the impact of AI-generated deepfakes on social media and in conflict zones.

“We’re getting into an era where we can no longer believe what we see,” says Marko Jak, co-founder and CEO of Secta Labs. “Right now, it’s easier because the deepfakes are not that good yet, and sometimes you can see it’s obvious.”

#NewProfilePic courtesy @sectalabs haha 🔥🔥 pic.twitter.com/WVrb24xGGc

— Marko (@markojak_) June 13, 2023

Jak speculates that we are nearing a point—possibly within a year—where discerning a fake image at first glance will be impossible.

His insights should be taken seriously as he is the CEO of Secta Labs, an AI-image generator company.

The Rising Concerns about Deepfakes

A recent trend in AI-generated deepfakes has sparked outrage and concern. Deepfakes of murder victims have been appearing online, designed to evoke strong emotional reactions and gain clicks and likes.

This alarming trend emphasizes the urgency for more efficient ways to detect deepfakes.

Jak’s Austin-based startup, Secta Labs, which he co-founded in 2023, focuses on creating high-quality AI-generated images.

Secta Labs views its users as the owners of the AI models generated from their data, while the company serves as custodians creating images from these models.

The Call for AI Regulation

The potential misuse of advanced AI models has prompted world leaders to push for immediate action on AI regulation.

This has also led to companies like Meta, the creators of the new AI-generated voice platform Voicebox, deciding against releasing their advanced tools to the public.

“It’s also necessary to strike the right balance between openness with responsibility,” a Meta spokesperson shared.

Deepfakes: A Tool for Misinformation

Earlier this month, the U.S. Federal Bureau of Investigation warned of AI deepfake extortion scams and criminals using photos and videos from social media to create fake content.

In the face of the growing deepfake problem, Jak suggests that the solution may not lie solely in detecting deepfakes, but rather in exposing them.

“AI is the first way you could spot [a deepfake],” Jak said. “There are people building artificial intelligence that you can put an image into like a video, and the AI can tell you if it was generated by AI.”

Technology to Counter Deepfakes

Jak acknowledges that an “AI arms race” is emerging with bad actors creating more sophisticated deepfakes to counter the technology designed to detect them.

Jak proposes that technology such as blockchain and cryptography might offer a solution to the deepfake problem by authenticating an image’s origin.

He also suggests a low-tech solution — harnessing the collective wisdom of internet users.

“A tweet can be misinformation just like a deepfake can be,” he said. Jak believes that social media platforms could benefit from leveraging their communities to verify whether the circulated content is genuine.

As AI advances, the battle against deepfakes continues, underlining the importance of both technological and social solutions to counter this growing issue.

Companies across the globe are increasingly recognizing the value of artificial intelligence (AI) and are willing to pay handsome salaries to professionals proficient in AI tools like ChatGPT.

These companies are offering an average salary of Rs 1.5 crore, with some even offering twice that amount.

Since its launch in 2022, ChatGPT, an AI chatbot developed by OpenAI, has revolutionized the tech industry.

Known for its ability to generate human-like text responses, the bot has found applications in a multitude of areas such as essay writing, music composition, and even poetry crafting.

As a result, expertise in this AI tool has become a hot commodity, opening up several job opportunities.

AI Creating Jobs

According to a study by ResumeBuilder, 91% of companies with job vacancies are seeking candidates skilled in ChatGPT, underscoring the belief that AI has the potential to increase productivity, save time, and enhance overall company performance.

A report by Business Insider indicates that companies listed on LinkedIn are ready to offer annual salaries of up to USD 185,000 (approximately Rs 1.5 crore) to individuals proficient in ChatGPT.

HR company Recruiting from Scratch, based in the US, is currently hiring for the position of Senior Machine Learning Engineer, Audio, with job requirements including familiarity with AI tools and platforms like ChatGPT.

The salary for this role ranges from USD 125,000 to USD 185,000 per year.

Interface.ai, a conversational AI tool, is seeking a Remote Machine Engineer with experience in natural language processing and large language models like ChatGPT, offering a salary up to USD 170,000 per year.

Emergence of Prompt Engineering

Despite concerns about AI leading to job displacement, ChatGPT has actually led to the creation of new job roles.

One such emerging profession is Prompt Engineering, which is growing in popularity.

Earlier this year, San Francisco-based AI startup Anthropic posted a job advertisement for a Prompt Engineer and a Librarian, offering a salary of up to USD 335,000 per year (approximately Rs 2.7 crore).

This demand for AI Prompt Engineering roles extends beyond San Francisco, with numerous job openings for prompt engineers found on platforms like LinkedIn and other job search websites.

Online platforms have also begun to offer courses focused on Prompt Engineering to cater to this growing demand.

Google’s DeepMind, the firm behind the historic victory of artificial intelligence (AI) over human intelligence in the complex board game Go, is gearing up for another breakthrough.

Demis Hassabis, the CEO of DeepMind, has revealed that they are developing a more powerful AI language model named Gemini, intended to surpass OpenAI’s ChatGPT in capabilities.

In 2016, DeepMind’s AI program, AlphaGo, astounded the world by defeating a world champion Go player.

Gemini, The Fusion of Advanced Techniques

Hassabis explained that the company is planning to leverage the techniques used in the creation of AlphaGo for the development of Gemini. Gemini, similar to GPT-4, is a large language model that works with text.

“At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models.”

Hassabi

The goal is to merge these technologies to equip Gemini with advanced features like problem-solving and planning.

The foundation of AlphaGo’s prowess was reinforcement learning, a method DeepMind has perfected.

The approach involves software learning to solve intricate tasks that require strategic decision-making through trial and error and performance feedback. AlphaGo also utilized a method called tree search to investigate and remember possible game moves.

The intent is to have Gemini imbibe similar methods and push the boundaries of language models, possibly even performing tasks on the internet and computers.

Hassabis indicated that the development of Gemini is a time-intensive process that might take several months and could involve significant funding.

Gemini’s Anticipated Capabilities

The completion of Gemini could significantly bolster Google’s response to the competition from ChatGPT and other AI technologies.

DeepMind was acquired by Google in 2014 after showcasing impressive results from software that employed reinforcement learning to master simple video games.

It demonstrated how AI can perform tasks once believed to be exclusive to human capabilities, often with superior proficiency.

DeepMind’s reinforcement learning expertise could potentially provide Gemini with unique abilities.

However, the journey towards AI advancements is fraught with risks and uncertainties. The rapid development in language models has raised concerns about misuse of technology and control issues.

Despite these concerns, Hassabis emphasized the extraordinary potential benefits of AI in areas such as healthcare and climate science, making it crucial not to halt its development.

Hassabis and his team, while acknowledging the challenges and potential risks, continue to forge ahead, focused on the development of more powerful and innovative AI models like Gemini.

The future of AI seems promising, yet the path ahead remains one of exploration and learning.

Embattled cryptocurrency exchange FTX has abruptly stopped the sale of its stake in AI firm Anthropic, valued over $500 million, as reported by Bloomberg on Tuesday.

FTX had filed for Chapter 11 bankruptcy protection last November, followed by allegations against co-founder Sam Bankman-Fried for money laundering, fraud, and conspiracy to commit wire fraud.

Court documents filed recently alleged that Bankman-Fried and other high-ranking executives at FTX were involved in commingling over $402 million in customer funds.

The collapsed exchange is reported to owe customers around $8.7 billion, with $6.4 billion in the form of fiat currency and stablecoins.

FTX’s Efforts to Repay Creditors

In January, FTX was given the green light by a federal judge to sell off some assets to repay creditors.

Among the assets FTX has offloaded is derivatives trading platform LedgerX, sold for $50 million – a stark loss compared to the $300 million FTX paid for LedgerX in 2021.

Earlier this month, FTX, with the assistance of financial services firm Perella Weinberg Partners, had shown intentions to offload its shares in Anthropic as part of its clawback strategy to repay creditors.

A clawback in bankruptcy is a legal process where a bankruptcy trustee retrieves property or payments made by the company prior to filing for bankruptcy.

Anthropic’s Success Story

Founded in 2021 by former OpenAI employees, Anthropic has become a front-runner in the current AI boom.

The firm launched Claude AI in March after receiving a $400 million investment from Google earlier this year, supplemented by another $450 million in Series C funding in May, led by Spark Capital.

In May, Anthropic made significant advancements in Claude AI, including the development of a rule-set based on the Universal Declaration of Human Rights, designed to promote ethical behavior and discourage undesirable actions.

A Pause on the Sale

The unexpected halt in the sale of Anthropic shares came after several potential buyers had evaluated private information about the stake.

Anthropic, which is privately-held, is currently valued at $4.6 billion, according to Semafor’s June report.

Buyers in the secondary market for shares in private companies have been eager to acquire stakes in Anthropic, even at a premium, indicating the significant potential of this AI startup.

FTX’s stake in Anthropic, amounting to a $500 million investment made by FTX and Alameda, is among FTX’s most coveted assets.

While the reason for this sudden halt remains undisclosed, it marks another twist in FTX’s ongoing legal and financial turmoil.

By Mukund Kapoor

OpenAI, a leading name in the AI industry, has unveiled its new web crawling bot, GPTBot, to broaden the dataset for training future AI systems, possibly including the next version named “GPT-5,” as indicated by a recent trademark application.

Gathering Public Data

The newly released GPTBot will collect publicly accessible data from websites while steering clear of paywalled, sensitive, and prohibited content.

Breaking 🚨

OpenAI just launched GPTBot, a web crawler designed to automatically scrape data from the entire internet.

This data will be used to train future AI models like GPT-4 and GPT-5!

GPTBot ensures that sources violating privacy and those behind paywalls are excluded. pic.twitter.com/oR3kY4buaU

— Shubham Saboo (@Saboo_Shubham_) August 7, 2023

This web crawler functions similarly to those of search engines like Google and Bing, assuming that accessible information is fair for use.

To block the OpenAI web crawler from accessing a site, the owner must add a “disallow” rule to a file on their server.

OpenAI has assured that GPTBot will scan the scraped data to eliminate any personally identifiable information (PII) or text that contradicts its policies.

However, the opt-out approach is generating ethical concerns around consent. Critics argue that OpenAI’s actions might lead to derivative work without proper citation.

Addressing Past Controversies

The launch follows prior criticism where OpenAI was accused of scraping data without permission for training its Large Language Models (LLMs) like ChatGPT.

In response, OpenAI updated its privacy policies in April.

The new web crawler represents OpenAI’s need for more current data to maintain and enhance its LLMs.

The move may indicate a shift from OpenAI’s initial focus on transparency and safety, understandable as ChatGPT remains the most used LLM globally.

OpenAI’s products heavily rely on the quality of data used for training, and the GPTBot aims to gather that essential data.

Competition in the AI Space

Meta, the social media titan, has also been working on AI, offering its model for free unless used by competitors or large businesses.

While OpenAI’s strategy revolves around using crawled data for profitable AI tool ecosystems, Meta aims to build a profitable business around its data.

OpenAI’s ChatGPT currently boasts over 1.5 billion monthly active users, and Microsoft’s $10 billion investment in OpenAI is paying off, as ChatGPT integration has enhanced Bing’s capabilities.

As OpenAI’s GPTBot represents an advancement in AI capabilities, it also reopens copyright, consent, and ethics debates.

As AI systems become more advanced, striking the right balance between transparency, ethics, and technological capability will continue to challenge industry leaders.

The new web crawler’s launch highlights the complexities of innovation in the AI space, where benefits in efficiency and ability may come with potential ethical trade-offs.