The UK's competition watchdog will not hold a formal investigation into Microsoft's partnership with the startup behind the artificial intelligence chatbot ChatGPT, stating that while the 2.9tn ( 2.3tn) tech company has "material influence" over OpenAI it does not control it. The Competition and Markets Authority (CMA) said Microsoft, OpenAI's biggest financial backer with a 13bn investment, acquired material influence over the San Francisco-based business in 2019 but did not exercise de facto control over it – and therefore did not meet the threshold for an official inquiry. The decision follows expressions of disquiet over the appointment of the former boss of Amazon UK, Doug Gurr, as the CMA's interim chair. The organisation's chief executive, Sarah Cardell, has also said the CMA does not want to create a "chilling effect" on business confidence, amid pressure from the UK government on regulators to produce pro-growth proposals. The CMA's executive director for mergers, Joel Bamford, said: "We have found that there has not been a change of control by Microsoft from material influence to de facto control over OpenAI. Because this change of control has not happened, the partnership in its current form does not qualify for review under the UK's merger control regime."

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As a precocious teen looking to improve my college application, I sat in on a business studies class. I figured taking two extra A-Levels at night school alongside those I took during the day would make me irresistible to admissions tutors. The class I watched examined if it was worth a large factory keeping its own trucks and drivers in-house rather than outsourcing them. The data showed selling the trucks and firing the workers was more expensive in the long run, and yoked the company to the whims of any third-party logistics company in the local area. Not to mention, if you don't own a mission-critical component of your business, you're a lot less powerful when negotiating with your suppliers.

Microsoft today said that it's extending its partnership with OpenAI, the startup behind art- and text-generating AI systems like ChatGPT, DALL-E 2 and GPT-3, with a "multi-year, multi-billion-dollar" investment. OpenAI says that the infusion of new capital -- the exact amount of which wasn't disclosed -- will be used to continue its independent research and develop AI that's "safe, useful and powerful." The optics aren't the best for Microsoft, which just last week announced plans to lay off 10,000 employees as a part of broader cost-cutting measures. But they'd been telegraphed by the company earlier this month -- in an interview with The Wall Street Journal, Microsoft CEO Satya Nadella said that Microsoft planned to make OpenAI's foundational systems available as commercials platforms so that any entity in any industry can build on them. OpenAI will remain a capped-profit company as a part of the new investment deal with Microsoft.

Luxonis, a Colorado-based robotic vision platform, has launched rae, its first "fully-formed and high-powered personal robot". Backed by a Kickstarter campaign to help support its development, rae sets itself apart by offering a multitude of features right out of the box, along with a unique degree of experimental programming potential that far exceeds other consumer robots on the market. The most recent of a long line of Luxonis innovations, rae is designed to make robotics accessible and simple for users of any experience level. Brandon Gilles, CEO of Luxonis, says: "rae is representative of our foremost goal at Luxonis: to make robotics accessible and simple for anyone, not just the tenured engineer with years of programming experience. "A longstanding truth about robotics is that the barrier to entry sometimes feels impossibly high, but it doesn't have to be that way.

Crowdfunding, which is the act of raising funds from a large number of people's contributions, is among the most popular research topics in economic theory. Due to the fact that crowdfunding platforms (CFPs) have facilitated the process of raising funds by offering several features, we should take their existence and survival in the marketplace into account. In this study, we investigated the significant role of platform features in a customer behavioral choice model. In particular, we proposed a multinomial logit model to describe the customers' (backers') behavior in a crowdfunding setting. We proceed by discussing the revenue-sharing model in these platforms. For this purpose, we conclude that an assortment optimization problem could be of major importance in order to maximize the platforms' revenue. We were able to derive a reasonable amount of data in some cases and implement two well-known machine learning methods such as multivariate regression and classification problems to predict the best assortments the platform could offer to every arriving customer. We compared the results of these two methods and investigated how well they perform in all cases.

Last week I wrote about an AI startup that's building technology that can alter, in real time, the accent of someone's speech. But what if the AI goal instead is to make it possible for people speaking in whatever way they do, to be understood just as they are, and to remove some of the bias inherent in a lot of AI systems in the process? There's a major need for that, too, and now a UK startup called Speechmatics -- which has built AI to translate speech to text, regardless of the accent or how the person speaks -- is announcing $62 million in funding to expand its business. Susquehanna Growth Equity out of the U.S. led the round with UK investors AlbionVC and IQ Capital also participating. This is Series B is a big step up for Speechmatics.

The news: WhyLabs, a spinout from the Seattle's Allen Institute for Artificial Intelligence (AI2), raised $10 million and released a new tool to support machine learning applications. The problem: As more companies leverage machine learning and artificial intelligence, the need to capture and correct failures is becoming more urgent. Reliance on algorithms can lead to negative implications, as evidenced this week by Zillow Group, for example. "The challenges begin once the machine learning system is live -- it automates millions of decisions a day," a WhyLabs spokesperson told GeekWire in an email. "Monitoring how well it's working becomes critical, because machine learning systems fail in often catastrophic ways."

Shelf Engine's mission to eliminate food waste in grocery retailers now has some additional celebrity backers. The company brought in a $2 million extension to its $41 million Series B announced in March. Ellen DeGeneres, Portia de Rossi, Shaun White and Shawn Mendes are the new backers, who came in through a strategic round of funding alongside PLUS Capital to bring the Seattle-based company's total funding to $60 million since the company's inception in 2016. This includes a $12 million Series A from 2020. Shelf Engine's grocery order automation technology applies advanced statistical models and artificial intelligence to deliver accurate food order volume so that customers can reduce their food waste by as much as 32% while increasing gross margins and sales of more than 50%.

"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models. This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful.