Microsoft in Advanced Talks to Increase Investment in OpenAI

Microsoft previously invested $1 billion in OpenAI.Photo:Mike Segar/REUTERS 

Microsoft Corp. MSFT 1.26% is in advanced talks for a new round of funding in OpenAI, according to a person familiar with the matter, as the software giant seeks to further incorporate artificial intelligence into its products.

No deal has been reached between the two sides and the funding amount could vary as negotiations evolve, the person said. The companies have held talks in recent weeks, according to people familiar with the matter. Microsoft invested $1 billion in OpenAI in 2019.

The new cash could help bankroll the tremendous computing power OpenAI needs to run its various artificial intelligence products on Azure, Microsoft’s cloud computing service.

The startup already uses Azure as its exclusive cloud partner and has agreed to give priority to Microsoft when bringing new technologies to market.Everything is now a tech thing. Columnist Joanna Stern is your guide, giving analysis and answering your questions about our always-connected world.

OpenAI earlier this year launched Dall-E 2, a project that allowed users to generate art from strings of text and showed the fast advances being made in that segment of AI technology. Last week, Microsoft announced it was integrating Dall-E 2 with various products including Microsoft Design, a new graphic design app, and the image creator for search app Bing.

Alphabet Inc.’s Google and Microsoft have each developed its own image-generating models—but have been slower to release them to the public, in part because of ethical concerns.

A newer set of startups have moved more quickly. In August, artificial intelligence startup Stability AI released its own text-to-image generator to the public. It then raised $101 million in new funding from investors including Lightspeed Venture Partners and Coatue Management.

OpenAI, led by the technology investor Sam Altman, began as a nonprofit in 2015 with $1 billion in pledges from Tesla Inc. CEO Elon Musk, LinkedIn co-founder Reid Hoffman and other backers.

In 2019, OpenAI created a for-profit company and raised the $1 billion from Microsoft, citing greater computing and staffing needs. OpenAI said it would cap profits at the company, diverting the remainder to the nonprofit group.

Much of OpenAI’s research is focused on the potential impacts of artificial general intelligence, the idea that AI systems could one day become better than humans at performing some of the most important tasks.

Some researchers have cautioned that recent advances in AI contribute to unwarranted hype around the technology, and many applications are fraught with ethical issues.

OpenAI said more than 1.5 million users created over 2 million images a day using Dall-E 2 after it was released to the public this month. The technology can turn text descriptions like “an Andy Warhol style painting of a bunny rabbit wearing sunglasses” into realistic images and artworks.

OpenAI has also developed programs to automatically generate text or computer code from written language samples, which are used by companies including Salesforce Inc. CRM 0.02% and Intel Corp. INTC 0.76%

By: Berber Jin and Miles Kruppa

Source: Microsoft in Advanced Talks to Increase Investment in OpenAI – WSJ

Critics:

Criticism of Microsoft has followed various aspects of its products and business practices. Frequently criticized are the ease of use, robustness, and security of the company’s software. They’ve also been criticized for the use of permatemp employees (employees employed for years as “temporary,” and therefore without medical benefits), the use of forced retention tactics, which means that employees would be sued if they tried to leave.[246]

Historically, Microsoft has also been accused of overworking employees, in many cases, leading to burnout within just a few years of joining the company. The company is often referred to as a “Velvet Sweatshop”, a term which originated in a 1989 Seattle Times article,[247] and later became used to describe the company by some of Microsoft’s own employees.[248] This characterization is derived from the perception that Microsoft provides nearly everything for its employees in a convenient place, but in turn overworks them to a point where it would be bad for their (possibly long-term) health.

As reported by several news outlets,[249][250] an Irish subsidiary of Microsoft based in the Republic of Ireland declared £220 bn in profits but paid no corporation tax for the year 2020. This is due to the company being tax resident in Bermuda as mentioned in the accounts for ‘Microsoft Round Island One’, a subsidiary that collects licence fees from the use of Microsoft software worldwide.

Dame Margaret Hodge, a Labour MP in the UK said, “It is unsurprising – yet still shocking – that massively wealthy global corporations openly, unashamedly and blatantly refuse to pay tax on the profits they make in the countries where they undertake business”.[250]

In 2020, ProPublica reported that the company had diverted more than $39 billion in U.S. profits to Puerto Rico using a mechanism structured to make it seem as if the company was unprofitable on paper. As a result, the company paid a tax rate on those profits of “nearly 0%.” When the Internal Revenue Service audited these transactions, ProPublica reported that Microsoft aggressively fought back, including successfully lobbying Congress to change the law to make it harder for the agency to conduct audits of large corporations.

“Embrace, extend, and extinguish” (EEE),[253] also known as “embrace, extend, and exterminate”,[254] is a phrase that the U.S. Department of Justice found[255] that was used internally by Microsoft[256] to describe its strategy for entering product categories involving widely used standards, extending those standards with proprietary capabilities, and then using those differences to strongly disadvantage competitors. Microsoft is frequently accused of using anticompetitive tactics and abusing its monopolistic power.

People who use their products and services often end up becoming dependent on them, a process known as vendor lock-in. Microsoft was the first company to participate in the PRISM surveillance program, according to leaked NSA documents obtained by The Guardian[257] and The Washington Post[258] in June 2013, and acknowledged by government officials following the leak.[259]

The program authorizes the government to secretly access data of non-US citizens hosted by American companies without a warrant. Microsoft has denied participation in such a program. Jesse Jackson believes Microsoft should hire more minorities and women. In 2015, he praised Microsoft for appointing two women to its board of directors

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AI Can Write Code Like Humans Bugs and All

Some software developers are now letting artificial intelligence help write their code. They’re finding that AI is just as flawed as humans.

Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest.

Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works. “It lets me spend less time jumping to the browser to look up API docs or examples on Stack Overflow,” he says. “It does feel a little like my work has shifted from being a generator of code to being a discriminator of it.”

But Naka has found that errors can creep into his code in different ways. “There have been times where I’ve missed some kind of subtle error when I accept one of its proposals,” he says. “And it can be really hard to track this down, perhaps because it seems like it makes errors that have a different flavor than the kind I would make.”

The risks of AI generating faulty code may be surprisingly high. Researchers at NYU recently analyzed code generated by Copilot and found that, for certain tasks where security is crucial, the code contains security flaws around 40 percent of the time.

The figure “is a little bit higher than I would have expected,” says Brendan Dolan-Gavitt, a professor at NYU involved with the analysis. “But the way Copilot was trained wasn’t actually to write good code—it was just to produce the kind of text that would follow a given prompt.”

Despite such flaws, Copilot and similar AI-powered tools may herald a sea change in the way software developers write code. There’s growing interest in using AI to help automate more mundane work. But Copilot also highlights some of the pitfalls of today’s AI techniques.

While analyzing the code made available for a Copilot plugin, Dolan-Gavitt found that it included a list of restricted phrases. These were apparently introduced to prevent the system from blurting out offensive messages or copying well-known code written by someone else.

Oege de Moor, vice president of research at GitHub and one of the developers of Copilot, says security has been a concern from the start. He says the percentage of flawed code cited by the NYU researchers is only relevant for a subset of code where security flaws are more likely.

De Moor invented CodeQL, a tool used by the NYU researchers that automatically identifies bugs in code. He says GitHub recommends that developers use Copilot together with CodeQL to ensure their work is safe.

The GitHub program is built on top of an AI model developed by OpenAI, a prominent AI company doing cutting-edge work in machine learning. That model, called Codex, consists of a large artificial neural network trained to predict the next characters in both text and computer code. The algorithm ingested billions of lines of code stored on GitHub—not all of it perfect—in order to learn how to write code.

OpenAI has built its own AI coding tool on top of Codex that can perform some stunning coding tricks. It can turn a typed instruction, such as “Create an array of random variables between 1 and 100 and then return the largest of them,” into working code in several programming languages.

Another version of the same OpenAI program, called GPT-3, can generate coherent text on a given subject, but it can also regurgitate offensive or biased language learned from the darker corners of the web.

Copilot and Codex have led some developers to wonder if AI might automate them out of work. In fact, as Naka’s experience shows, developers need considerable skill to use the program, as they often must vet or tweak its suggestions.

Hammond Pearce, a postdoctoral researcher at NYU involved with the analysis of Copilot code, says the program sometimes produces problematic code because it doesn’t fully understand what a piece of code is trying to do. “Vulnerabilities are often caused by a lack of context that a developer needs to know,” he says.

Some developers worry that AI is already picking up bad habits. “We have worked hard as an industry to get away from copy-pasting solutions, and now Copilot has created a supercharged version of that,” says Maxim Khailo, a software developer who has experimented with using AI to generate code but has not tried Copilot.

Khailo says it might be possible for hackers to mess with a program like Copilot. “If I was a bad actor, what I would do would be to create vulnerable code projects on GitHub, artificially boost their popularity by buying GitHub stars on the black market, and hope that it will become part of the corpus for the next training round.”

Both GitHub and OpenAI say that, on the contrary, their AI coding tools are only likely to become less error prone. OpenAI says it vets projects and code both manually and using automated tools.

De Moor at GitHub says recent updates to Copilot should have reduced the frequency of security vulnerabilities. But he adds that his team is exploring other ways of improving the output of Copilot. One is to remove bad examples that the underlying AI model learns from. Another may be to use reinforcement learning, an AI technique that has produced some impressive results in games and other areas, to automatically spot bad output, including previously unseen examples. “Enormous improvements are happening,” he says. “It’s almost unimaginable what it will look like in a year.”

Source: AI Can Write Code Like Humans—Bugs and All | WIRED

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