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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.

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.”

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.

Meta, a leading name in the tech industry, has made a significant leap in artificial intelligence (AI) by developing Voicebox, an advanced tool capable of generating lifelike speech.

Despite the tool’s potential, the company has chosen not to release it immediately due to concerns about potential misuse.

Voicebox

Announced last Friday, Voicebox can create convincing voice dialogue, opening up a range of possibilities, from enhancing communication across languages to delivering lifelike character dialogue in video games.

Unique in its functionality, Voicebox can generate speech it wasn’t specifically trained for.

All it requires is some text input and a small audio clip, which it then uses to create a whole new speech in the voice of the source audio.

In a breakthrough from traditional AI speech tools, Voicebox learns directly from raw audio and its corresponding transcription, eliminating the need for task-specific training with carefully curated datasets.

Moreover, this impressive tool can produce audio in six languages – English, French, German, Spanish, Polish, and Portuguese – offering a realistic representation of natural human speech.

Potential Misuse and Meta’s Precautionary Approach

While Voicebox opens up exciting possibilities, Meta is fully aware of the potential misuse of such a tool.

The AI tool could be misused to create ‘deepfake’ dialogues, replicating the voices of public figures or celebrities in an unethical manner.

To counter this risk, Meta has developed AI classifiers, akin to spam filters, that can differentiate between human speech and speech generated by ‘Voicebox’.

The company is advocating for transparency in AI development, coupled with a firm commitment to responsible use. As part of this commitment, Meta has no current plans to make ‘Voicebox’ publicly available, emphasizing the need to balance openness with responsibility.

Instead of launching a functional tool, Meta is offering audio samples and a research paper to help researchers understand its potential and work towards responsible use.

Global Concerns Over AI Misuse

The rapid advancements in AI are causing concern among global leaders, including the United Nations (UN).

Deepfakes have been utilized in scams and have propagated hate and misinformation online, as highlighted in a recent UN report.

Creating AI tools like ‘Voicebox’ offers numerous possibilities but underscores the importance of cautious development and responsible use to prevent misuse.

As we continue to stride forward in the field of AI, these concerns will remain paramount.

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.