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Did I write this, or was it ChatGPT?

Its hard to tell, isnt it?

For the sake of my editors, I will follow that quickly with: I wrote this article (I swear). But the point is that its worth exploring generative artificial intelligences limitations and areas of utility for developers and users. Both are revealing. The same is true for Web3 and blockchain.

While were already seeing the practical applications of Web3 and generative AI play out in tech platforms, online interactions, scripts, games and social media apps, were also seeing a replay of the responsible AI and blockchain 1.0 hype cycles of the mid-2010s.

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We need a set of principles or ethics to guide innovation. We need more regulation. We need less regulation. There are bad actors poisoning the well for the rest of us. We need heroes to save us from AI and/or blockchain. Technology is too sentient. Technology is too limited. There is no enterprise-level application. There are countless enterprise-level applications.

If you exclusively read the headlines, you will come out the other side with the conclusion that the combo of generative AI and blockchain will either save the world or destroy it.

Weve seen this play (and every act and intermission) before with the hype cycles of both responsible AI and blockchain. The only difference this time is that the articles were reading about ChatGPTs implications may, in fact, have been written by ChatGPT. And the term blockchain has a bit more heft behind it thanks to investment from Web2 giants like Google Cloud, Mastercard and Starbucks.

That said, its notable that OpenAIs leadership recently called for an international regulatory body akin to the International Atomic Energy Agency (IAEA) to regulate and, when necessary, rein in AI innovation. The proactive move illuminates an awareness of both AIs massive potential and potentially society-crumbling pitfalls. It also conveys that the technology itself is still in test mode.

The other significant subtext: Public sector regulation at the federal and sub-federal levels commonly limits innovation.

As with Web3, and whether or not regulatory action takes place, responsibility needs to be at the core of generative AI innovation and adoption. As the technology evolves rapidly, its important for vendors and platforms to assess every potential use case to ensure responsible experimentation and adoption. And, as OpenAIs Sam Altman and Googles Sundar Pichai notably point out, working with the public sector to evolve regulation is a significant part of that equation.

Its also important to surface limitations, transparently report on them, and provide guardrails if or when issues become apparent.

While AI and blockchain have both been around for decades, the impact of AI, in particular, is now visible with ChatGPT, Bard and the entire field of generative AI players. Together with Web3s decentralized power, were about to witness an explosion of practical applications that build on progress automating interactions and advancing Web3 in more visible ways.

From a user-centric perspective (and whether we know it or not), generative AI and blockchain are both already transforming how people interact in the real world and online. Solana recently made it official with a ChatGPT integration. And exchange Bitget backed away from theirs.

Promising or puzzling, every signal indicates that it remains to be seen where the technologies best intersect in the name of user experience and user-centric innovation. From where I sit as the head of a layer1 blockchain built for scale and interoperability, the question becomes: How should AI and blockchain join forces in pursuit of Web3s own ChatGPT moment of mainstream adoption?

Tools like ChatGPT and Bard will accelerate the next major waves of innovation on Web2 and Web3. The convergence of generative AI and Web3 will be like the pairing of peanut butter and jelly on fresh bread but, you know, with code, infrastructure, and asset portability. And, as hype is replaced with practical applications and constant upgrades, persistent questions about whether these technologies will take hold in the mainstream will be toast.

Enterprise leaders should view generative AI as a tool worth exploring, testing, and after doing both, integrating. Specifically, they should focus efforts on exploring how the generative element can improve work outcomes internally with teams and externally with customers or partners. And they should continuously map out its enterprise-wide potential and limitations.

Its time to begin to map out and document where not to use generative AI, which is equally important in my book. Dont rely on the technology for anything where you need to apply facts and hard data to outputs for community members, partners, teams or investors, and dont rely on it for protocol upgrades, software engineering, coding sprints or international business operations.

On a practical level, enterprise leaders should consider incorporating generative AI into administrative workflows to keep their companys day-to-day workflows moving faster and more efficiently. Explore its seemingly universal utility to kick off text- or code-heavy projects across engineering, marketing, business and executive functions. And since this tech changes by the day, enterprise leaders should look at every possible new use case to decide whether to responsibly experiment with it en route to adoption, which also applies to work in Web3.

Mo Shaikh is cofounder and CEO of Aptos Labs.

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Excerpt from:

Generative AI and Web3: Hyped nonsense or a match made in tech ... - VentureBeat

Artificial intelligenceis poised to be the next big thing in a bunch of different industries, including the entertainment sector. But the workers AI might one day replace are fighting for a say in how the technology is usedbefore it gets too big to stop.

Generative AI, meaning AI that can create text, images, and other content, can sometimes feel like a magic boxgive it a prompt, and itll spit out a more-or-less correct response that looks like its been written by a person.

The technologys ability to easily churn out human-quality work for cheap has many artists and writers worried. Artificial intelligence isnt going to replace screenwriters wholesale any time soon, but it could still undermine creative jobs by giving production studios a cheap way to underpay writers.

Bryan Sullivan is a lawyer who specializes in crisis management for the entertainment industry. He told Next Level, "I don't think people realized until recently thatwriters view AI as a threat.The studios could cut the first layer of writing out by using an AI system and then hiring a writer to do a polish, which is a lot less money."

SEE MORE: Hollywood and a history of strikes: How did they turn out?

The potential threat of AI is one issue behind the Writers Guild of Americas most recent strike. Part of the unions demands when they struck aimed to limit studios ability to use AI to cut costs on projects.

AI fears also motivated actors to walk off the job alongside the writers. The actors union, SAG-AFTRA, cited concerns that actors performances could be replicated by artificial intelligence as one justification for their strike.

Writers in Hollywood have already seen their contracts and opportunities shrink in the face of studios efforts to save money. In that environment, its hard not to look at AI in Hollywood as less of a creative engine and more of a cost-cutting measure.

Helen Silverstein is a video game writer and the co-chair of DSA-LA's Hollywood Labor Committee. She told Next Level:"So many writers who despite writing on Emmy award winning shows, are on food stamps or struggling, living paycheck to paycheck, struggling to survive. It is not just about writing or even just creativity at all. It's about working people being able to live, and create, and work, survive, and thrive."

There may not be a whole lot workers can do to protect themselves from being replaced; strikes only work when AI isnt developed enough to cross the picket line. Strikes and protests from workers might not change how the technology behind AI develops, but they can try to shape how its used by the profit-driven industries around them.

Trending stories at Scrippsnews.com

Read the original here:

AI fears are fueling the labor strikes in Hollywood - LEX 18 News - Lexington, KY

Anyone seduced by A.I.-powered chatbots like ChatGPT and Bard wow, they can write essays and recipes! eventually runs into what are known as hallucinations, the tendency for artificial intelligence to fabricate information.

The chatbots, which guess what to say based on information obtained from all over the internet, cant help but get things wrong. And when they fail by publishing a cake recipe with wildly inaccurate flour measurements, for instance it can be a real buzzkill.

Yet as mainstream tech tools continue to integrate A.I., its crucial to get a handle on how to use it to serve us. After testing dozens of A.I. products over the last two months, I concluded that most of us are using the technology in a suboptimal way, largely because the tech companies gave us poor directions.

The chatbots are the least beneficial when we ask them questions and then hope whatever answers they come up with on their own are true, which is how they were designed to be used. But when directed to use information from trusted sources, such as credible websites and research papers, A.I. can carry out helpful tasks with a high degree of accuracy.

If you give them the right information, they can do interesting things with it, said Sam Heutmaker, the founder of Context, an A.I. start-up. But on their own, 70 percent of what you get is not going to be accurate.

With the simple tweak of advising the chatbots to work with specific data, they generated intelligible answers and useful advice. That transformed me over the last few months from a cranky A.I. skeptic into an enthusiastic power user. When I went on a trip using a travel itinerary planned by ChatGPT, it went well because the recommendations came from my favorite travel websites.

Directing the chatbots to specific high-quality sources like websites from well-established media outlets and academic publications can also help reduce the production and spread of misinformation. Let me share some of the approaches I used to get help with cooking, research and travel planning.

Chatbots like ChatGPT and Bard can write recipes that look good in theory but dont work in practice. In an experiment by The New York Timess Food desk in November, an early A.I. model created recipes for a Thanksgiving menu that included an extremely dry turkey and a dense cake.

I also ran into underwhelming results with A.I.-generated seafood recipes. But that changed when I experimented with ChatGPT plug-ins, which are essentially third-party apps that work with the chatbot. (Only subscribers who pay $20 a month for access to ChatGPT4, the latest version of the chatbot, can use plug-ins, which can be activated in the settings menu.)

On ChatGPTs plug-ins menu, I selected Tasty Recipes, which pulls data from the Tasty website owned by BuzzFeed, a well-known media site. I then asked the chatbot to come up with a meal plan including seafood dishes, ground pork and vegetable sides using recipes from the site. The bot presented an inspiring meal plan, including lemongrass pork banh mi, grilled tofu tacos and everything-in-the-fridge pasta; each meal suggestion included a link to a recipe on Tasty.

For recipes from other publications, I used Link Reader, a plug-in that let me paste in a web link to generate meal plans using recipes from other credible sites like Serious Eats. The chatbot pulled data from the sites to create meal plans and told me to visit the websites to read the recipes. That took extra work, but it beat an A.I.-concocted meal plan.

When I did research for an article on a popular video game series, I turned to ChatGPT and Bard to refresh my memory on past games by summarizing their plots. They messed up on important details about the games stories and characters.

After testing many other A.I. tools, I concluded that for research, it was crucial to fixate on trusted sources and quickly double-check the data for accuracy. I eventually found a tool that delivers that: Humata.AI, a free web app that has become popular among academic researchers and lawyers.

The app lets you upload a document such as a PDF, and from there a chatbot answers your questions about the material alongside a copy of the document, highlighting relevant portions.

In one test, I uploaded a research paper I found on PubMed, a government-run search engine for scientific literature. The tool produced a relevant summary of the lengthy document in minutes, a process that would have taken me hours, and I glanced at the highlights to double-check that the summaries were accurate.

Cyrus Khajvandi, a founder of Humata, which is based in Austin, Texas, developed the app when he was a researcher at Stanford and needed help reading dense scientific articles, he said. The problem with chatbots like ChatGPT, he said, is that they rely on outdated models of the web, so the data may lack relevant context.

When a Times travel writer recently asked ChatGPT to compose a travel itinerary for Milan, the bot guided her to visit a central part of town that was deserted because it was an Italian holiday, among other snafus.

I had better luck when I requested a vacation itinerary for me, my wife and our dogs in Mendocino County, Calif. As I did when planning a meal, I asked ChatGPT to incorporate suggestions from some of my favorite travel sites, such as Thrillist, which is owned by Vox, and The Timess travel section.

Within minutes, the chatbot generated an itinerary that included dog-friendly restaurants and activities, including a farm with wine and cheese pairings and a train to a popular hiking trail. This spared me several hours of planning, and most important, the dogs had a wonderful time.

Google and OpenAI, which works closely with Microsoft, say they are working to reduce hallucinations in their chatbots, but we can already reap A.I.s benefits by taking control of the data that the bots rely on to come up with answers.

To put it another way: The main benefit of training machines with enormous data sets is that they can now use language to simulate human reasoning, said Nathan Benaich, a venture capitalist who invests in A.I. companies. The important step for us, he said, is to pair that ability with high-quality information.

Original post:

How to Direct A.I. Chatbots to Make Them More Useful - The New York Times

Elon Musk recently announced his latest startup, xAI, will be focused on artificial intelligence. According to the companys website, the goal of xAI is to understand the true nature of the universe. The xAI team will be led by Musk and others who have previously worked with OpenAI, DeepMind, Google Research, Microsoft Research, and Tesla.

Musks new AI venture is the latest in a list of companies he has founded and leads including Tesla, SpaceX, and The Boring Company.

As of July 2023, Musk is the richest person in the world, with a net worth of $254 billion. Heres how the Tesla CEO and Twitter owner made his billions.

Tesla is the largest carmaker in the world by market value. The company builds and designs fully electric vehicles (EV) and energy generation and storage systems. Its cars include sedans and compact and mid-size SUVs.

Tesla (TSLA) was founded in 2003 as Tesla Motors by Martin Eberhard and Marc Tarpenning. Musk invested in the company and was a member of the board starting in 2004, and later became CEO in 2008.

Musk was allowed to claim the title of cofounder, thanks to an out-of-court settlement. Tesla went public in an initial public offering (IPO) on June 29, 2010. In 2021, Tesla moved its headquarters from its native Palo Alto, California, to Austin, Texas.

In July 2023, Tesla unveiled its first Cybertruck built in its Texas factory, almost two years behind the original schedule.

Musk has a 13% ownership stake in Tesla, worth $108 billion. In 2022, Teslas total revenue was $81.46 billion.

Musk is also the cofounder and CEO of SpaceX, a rocket manufacturing company that counts NASA as one of its clients, and helps resupply the space station.

SpaceX is valued at $137 billion as of January 2023 and raised $2.2 billion in 2022, making it the most valuable private company in the country. Musk owns 42% of SpaceX, which launched its 200th rocket in January and has more than 1 million subscribers for its Starlink internet service.

In April 2022, Musk bought Twitter for $44 million after threatening a hostile takeover. The deal was finalized in October 2022, after Twitter sued Musk for trying to back out of the deal.

His takeover has been controversial, as he laid off half of the companys workforce and added a paid subscription service ($8 per month) for anyone who wants their account verified. Musk owns about 79% of Twitter. The company is valued at about $20 billion as of March 2023.

Musk is also the founder of The Boring Company, a tunnel construction company that aims to solve traffic by building freight tunnels. The company raised $675 million in April 2022, at a valuation of $5.7 billion, according to Forbes.

Musk also co-founded a company called Neuralink which designed a "brain-computer interface," a chip that can be implanted into the brain. Neuralink is valued at about $5 billion, according to reporting by Reuters.

Link:

Elon Musk Launched His Own AI StartupHere's Musk's Net Worth - Investopedia

Inside a cavernous room this week in a one-story building in Santa Clara, Calif., six-and-a-half-foot-tall machines whirred behind white cabinets. The machines made up a new supercomputer that had become operational just last month.

The supercomputer, which was unveiled on Thursday by Cerebras, a Silicon Valley start-up, was built with the companys specialized chips, which are designed to power artificial intelligence products. The chips stand out for their size like that of a dinner plate, or 56 times as large as a chip commonly used for A.I. Each Cerebras chip packs the computing power of hundreds of traditional chips.

Cerebras said it had built the supercomputer for G42, an A.I. company. G42 said it planned to use the supercomputer to create and power A.I. products for the Middle East.

What were showing here is that there is an opportunity to build a very large, dedicated A.I. supercomputer, said Andrew Feldman, the chief executive of Cerebras. He added that his start-up wanted to show the world that this work can be done faster, it can be done with less energy, it can be done for lower cost.

Demand for computing power and A.I. chips has skyrocketed this year, fueled by a worldwide A.I. boom. Tech giants such as Microsoft, Meta and Google, as well as myriad start-ups, have rushed to roll out A.I. products in recent months after the A.I.-powered ChatGPT chatbot went viral for the eerily humanlike prose it could generate.

But making A.I. products typically requires significant amounts of computing power and specialized chips, leading to a ferocious hunt for more of those technologies. In May, Nvidia, the leading maker of chips used to power A.I. systems, said appetite for its products known as graphics processing units, or GPUs was so strong that its quarterly sales would be more than 50 percent above Wall Street estimates. The forecast sent Nvidias market value soaring above $1 trillion.

For the first time, were seeing a huge jump in the computer requirements because of A.I. technologies, said Ronen Dar, a founder of Run:AI, a start-up in Tel Aviv that helps companies develop A.I. models. That has created a huge demand for specialized chips, he added, and companies have rushed to secure access to them.

To get their hands on enough A.I. chips, some of the biggest tech companies including Google, Amazon, Advanced Micro Devices and Intel have developed their own alternatives. Start-ups such as Cerebras, Graphcore, Groq and SambaNova have also joined the race, aiming to break into the market that Nvidia has dominated.

Chips are set to play such a key role in A.I. that they could change the balance of power among tech companies and even nations. The Biden administration, for one, has recently weighed restrictions on the sale of A.I. chips to China, with some American officials saying Chinas A.I. abilities could pose a national security threat to the United States by enhancing Beijings military and security apparatus.

A.I. supercomputers have been built before, including by Nvidia. But its rare for start-ups to create them.

Cerebras, which is based in Sunnyvale, Calif., was founded in 2016 by Mr. Feldman and four other engineers, with the goal of building hardware that speeds up A.I. development. Over the years, the company has raised $740 million, including from Sam Altman, who leads the A.I. lab OpenAI, and venture capital firms such as Benchmark. Cerebras is valued at $4.1 billion.

Because the chips that are typically used to power A.I. are small often the size of a postage stamp it takes hundreds or even thousands of them to process a complicated A.I. model. In 2019, Cerebras took the wraps off what it claimed was the largest computer chip ever built, and Mr. Feldman has said its chips can train A.I. systems between 100 and 1,000 times as fast as existing hardware.

G42, the Abu Dhabi company, started working with Cerebras in 2021. It used a Cerebras system in April to train an Arabic version of ChatGPT.

In May, G42 asked Cerebras to build a network of supercomputers in different parts of the world. Talal Al Kaissi, the chief executive of G42 Cloud, a subsidiary of G42, said the cutting-edge technology would allow his company to make chatbots and to use A.I. to analyze genomic and preventive care data.

But the demand for GPUs was so high that it was hard to obtain enough to build a supercomputer. Cerebrass technology was both available and cost-effective, Mr. Al Kaissi said. So Cerebras used its chips to build the supercomputer for G42 in just 10 days, Mr. Feldman said.

The time scale was reduced tremendously, Mr. Al Kaissi said.

Over the next year, Cerebras said, it plans to build two more supercomputers for G42 one in Texas and one in North Carolina and, after that, six more distributed across the world. It is calling this network Condor Galaxy.

Start-ups are nonetheless likely to find it difficult to compete against Nvidia, said Chris Manning, a computer scientist at Stanford whose research focuses on A.I. Thats because people who build A.I. models are accustomed to using software that works on Nvidias A.I. chips, he said.

Other start-ups have also tried entering the A.I. chips market, yet many have effectively failed, Dr. Manning said.

But Mr. Feldman said he was hopeful. Many A.I. businesses do not want to be locked in only with Nvidia, he said, and there is global demand for other powerful chips like those from Cerebras.

We hope this moves A.I. forward, he said.

Read more from the original source:

An A.I. Supercomputer Whirs to Life, Powered by Giant Computer ... - The New York Times