A federal judge Tuesday sentenced former Google engineer Anthony Levandowski to 18 months in prison for stealing a trade secret from Google related to self-driving cars months before becoming the head of Uber’s rival unit.
U.S. District Judge William Alsup in San Francisco said Levandowski, who was convicted after a March plea agreement, could enter custody once the COVID-19 pandemic has subsided.
Alsup said a sentence short of imprisonment would have given “a green light to every future brilliant engineer to steal trade secrets,” comparing what Levandowski took to a “competitor’s game plan.”
The 75-year-old judge, who has been involved in Silicon Valley litigation for nearly five decades, described Levandowski’s conviction as the “biggest trade secret crime I have ever seen.”
“Billions [of dollars] in the future were at play, and when those kind of financial incentives are there good people will do terrible things, and that’s what happened here,” Alsup said.
Prosecutors sought a 27-month prison sentence.
Levandowski requested one-year confinement at his Marin County home, contending that bouts with pneumonia in recent years would make him susceptible to death from the novel coronavirus while in prison. His attorneys asked the judge to consider that investigators found no evidence that “Levandowski used any of Google’s trade secrets after leaving Google’s employment.”
Levandowski transferred more than 14,000 Google files including development schedules and product designs to his personal laptop before leaving the company and while negotiating a deal with Uber, where he briefly led its self-driving car unit.
Uber fired Levandowski in 2017 and then settled a lawsuit from Google parent Alphabet over the misuse of trade secrets, setting back the ride-hailing company’s self-driving project.
The dispute between the companies is ongoing. Levandowski filed for bankruptcy in March because he owes $179 million to Google for his actions before resigning in January 2016.
Google last week asked the bankruptcy judge to reject Uber’s argument that it is not responsible for paying the $179 million under his old employment agreement.
Levandowski, who now runs self-driving truck company Pronto, apologized to Google and said he plans to share his story of regret with others in the tech industry.
“Today marks the end of three and a half long years and the beginning of another long road ahead,” he said in a statement.
Is Fuel Tech (NASDAQ:FTEK) Using Debt Sensibly? – Simply Wall St News
Legendary fund manager Li Lu (who Charlie Munger backed) once said, ‘The biggest investment risk is not the volatility of prices, but whether you will suffer a permanent loss of capital.’ When we think about how risky a company is, we always like to look at its use of debt, since debt overload can lead to ruin. We can see that Fuel Tech, Inc. (NASDAQ:FTEK) does use debt in its business. But should shareholders be worried about its use of debt?
What Risk Does Debt Bring?
Generally speaking, debt only becomes a real problem when a company can’t easily pay it off, either by raising capital or with its own cash flow. If things get really bad, the lenders can take control of the business. However, a more frequent (but still costly) occurrence is where a company must issue shares at bargain-basement prices, permanently diluting shareholders, just to shore up its balance sheet. Of course, the upside of debt is that it often represents cheap capital, especially when it replaces dilution in a company with the ability to reinvest at high rates of return. When we think about a company’s use of debt, we first look at cash and debt together.
What Is Fuel Tech’s Debt?
The image below, which you can click on for greater detail, shows that at June 2020 Fuel Tech had debt of US$1.56m, up from none in one year. But it also has US$8.25m in cash to offset that, meaning it has US$6.70m net cash.
How Strong Is Fuel Tech’s Balance Sheet?
The latest balance sheet data shows that Fuel Tech had liabilities of US$5.37m due within a year, and liabilities of US$2.06m falling due after that. Offsetting this, it had US$8.25m in cash and US$5.73m in receivables that were due within 12 months. So it actually has US$6.55m more liquid assets than total liabilities.
This luscious liquidity implies that Fuel Tech’s balance sheet is sturdy like a giant sequoia tree. Having regard to this fact, we think its balance sheet is just as strong as misogynists are weak. Succinctly put, Fuel Tech boasts net cash, so it’s fair to say it does not have a heavy debt load! There’s no doubt that we learn most about debt from the balance sheet. But it is future earnings, more than anything, that will determine Fuel Tech’s ability to maintain a healthy balance sheet going forward. So if you’re focused on the future you can check out this free report showing analyst profit forecasts.
In the last year Fuel Tech had a loss before interest and tax, and actually shrunk its revenue by 62%, to US$20m. That makes us nervous, to say the least.
So How Risky Is Fuel Tech?
We have no doubt that loss making companies are, in general, riskier than profitable ones. And in the last year Fuel Tech had an earnings before interest and tax (EBIT) loss, truth be told. Indeed, in that time it burnt through US$4.3m of cash and made a loss of US$11m. While this does make the company a bit risky, it’s important to remember it has net cash of US$6.70m. That means it could keep spending at its current rate for more than two years. Even though its balance sheet seems sufficiently liquid, debt always makes us a little nervous if a company doesn’t produce free cash flow regularly. The balance sheet is clearly the area to focus on when you are analysing debt. However, not all investment risk resides within the balance sheet – far from it. Consider for instance, the ever-present spectre of investment risk. We’ve identified 3 warning signs with Fuel Tech , and understanding them should be part of your investment process.
If, after all that, you’re more interested in a fast growing company with a rock-solid balance sheet, then check out our list of net cash growth stocks without delay.
If you’re looking to trade Fuel Tech, open an account with the lowest-cost* platform trusted by professionals, Interactive Brokers. Their clients from over 200 countries and territories trade stocks, options, futures, forex, bonds and funds worldwide from a single integrated account.
This article by Simply Wall St is general in nature. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.
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Mozilla retires Firefox Send and Firefox Notes services
Mozilla shut down Firefox Send on September 17, 2020 and plans to shut down Firefox Notes no November 1, 2020. Firefox Send was launched as an experiment in 2017 to improve the sending of files on the Internet. It was the first experiment that Mozilla created that was created as a web service and not an add-on or feature for Firefox.
The service launched officially in 2019; it allowed anyone to share files with a size of up to 1 Gigabyte and Firefox account users to share files up to 2.5 Gigabyte in size. Users with a Firefox account got extra features such as the managing of all files uploaded to the service or changing expiration times.
One of the main features of Firefox Send was that files uploaded to the service expired after a certain number of downloads or an expiration time.
Mozilla pulled the service earlier this year promising that it would return at a later point in time. The main reason for the taking down was that bad actors used the platform to distribute malware and other unwanted files. The free nature of the service coupled with automatic deletions of files after a number of downloads or a set expiration time benefited bad actors significantly.
Firefox Send’s homepage redirects to the main Mozilla homepage. A support article on Mozilla’s support website provides confirmation that the service has been retired for good. A blog post on Mozilla’s official blog reveals additional information.
According to the post on the blog, Firefox Send is being retired as part of Mozilla’s restructuring. The company laid off 250 employees this year and announced that it would “tighten and refine its product focus”.
Firefox Send was a promising tool for encrypted file sharing. Send garnered good reach, a loyal audience, and real signs of value throughout its life. Unfortunately, some abusive users were beginning to use Send to ship malware and conduct spear phishing attacks. This summer we took Firefox Send offline to address this challenge.
In the intervening period, as we weighed the cost of our overall portfolio and strategic focus, we made the decision not to relaunch the service.
Firefox Notes was launched as an experiment in 2017 as well. Unlike Firefox Send, which was a web service decoupled from Firefox, Firefox Notes was an internal feature of Firefox that added note taking to the browser. Most browsers rely on third-party extensions for note taking functionality; Vivaldi is an exception to that as it launched with note taking support.
Notes was updated several times to improve functionality. The latest version supports multiple notes, formatting options, and the exporting of notes as HTML files.
Mozilla will shut down Firefox Notes on November 1, 2020. The Firefox browser extension will remain installed but it won’t be available anymore for installation. Mozilla won’t provide updates, security or otherwise, for the browser extension anymore, and Notes syncing will not be available. The Notes application for Android won’t work anymore at that time.
Firefox users who do use Notes may use the Export All Notes option of the extension to save all notes as HTML. All that is required for that is to click on the three dots in the notes extension interface and select the option. Individual notes can also be exported.
Now You: Have you used Firefox Send or Notes? What is your take on the termination of these services?
Have you read something written by GPT-3? Probably not, but it’s hard to be sure
In August, a blog post about personal productivity surfaced at the top of Hacker News, a website and message board well known in Silicon Valley circles that focuses on computer science and entrepreneurship.
There was nothing terribly unique about the post. It offered simple, relatively common advice. Some readers discussed its merits in comments and offered their own thoughts about how to be more productive.
A few people, however, found the post a little suspicious.
“This is either something written by GPT-3, or the human equivalent,” one person commented. “Zero substantive content, pure regurgitation.”
Another person had the same assessment: “I think this was written by GPT-3.”
It turns out that they were right. The blog post was written almost entirely by a piece of software called Generative Pre-trained Transformer 3, or GPT-3. Liam Porr, a computer science student at the University of California, Berkeley, used the new machine learning model to generate the post with the intention of fooling the public into believing it was the product of a human mind.
He wanted to see just how good GPT-3 is.
“With something like a language model, there’s not a good quantitative way to see how good it is, because language and writing is qualitative,” Porr said. “With this kind of experiment, I can concretely say 20,000 unique people came to my website and only three actually had the sense to say it was written by a robot.”
GPT-3 is not the first natural language program of its kind, but it has already gotten widespread attention for how good it is at mimicking simple human writing. But its release into the world, while not entirely public, has caused some concern that it could be used to quickly and cheaply generate misinformation or propaganda. While it was a harmless experiment, Porr’s post offered a concrete example of the risk.
And it adds GPT-3 to other pieces of advanced software that have been disseminated through the internet and have caused alarm. Deepfake technology, which can make doctored videos of people, has become common enough to spur congressional hearings.
But the technology has also been warmly welcomed by some technologists who are already using it to automate parts of their operations.
OpenAI, an artificial intelligence research lab, announced GPT-3 in July. To simulate human language, the autocomplete model was trained on a massive dataset — 60 million domains on the internet and the sites they navigate to, as well as other sites and text the researchers spoon-fed it.
The program can’t think for itself. Instead, it can take a simple thought from a person and guess what will come next.
People interested in experimenting with the language generator can request access from the research lab. Porr himself said it was pretty easy for him to gain access thanks to his connections to his college machine-learning community. OpenAI has also made GPT-3’s API commercially available.
Based on the numerous use cases popping up online, everyone from hobbyists to machine learning experts to a friend of a friend of a machine learning expert hasn’t had too much trouble gaining access to the simple yet powerful piece of tech.
Francis Jervis, founder of the automated luxury tenants rights organization Augrented, is one of those who gained early access to GPT-3.
Jervis uses the tool to help tenants automate eviction forms to landlords. Renters are able to enter four to five bullet points arguing against their eviction, and GPT-3 will generate a paragraph that fits into a negotiation letter template. Jervis notes that users should still fact-check the text that the language model generates.
“It’ll occasionally add in creative details, which might not be 100 percent appropriate to use in this kind of context,” he said. “This is why I wouldn’t use it for DIY legal documentation.”
Qasim Munye, who is studying medicine at Kings College London, applied for access to GPT-3 as soon as it was released and was approved a few weeks later. He built the tool into his existing app, Shortly, which helps users write short stories.
“For me it’s like, I might not want to use this tech where truth is important,” he said. “A lot of the times it will be very confidently wrong, a very wrong answer but in a confident way, so I wanted a use case where truth wasn’t important, and fiction is one example of that.”
GPT-3’s potential to be profoundly inaccurate hasn’t inhibited those with access to use it as a source for knowledge and enlightenment. Learn From Anyone, a software that creates one-on-one conversations with any famous or historical figures, is powered by GPT-3.
“Ever wanted to learn about rockets from Elon Musk? How to write better from Shakespeare? Philosophy from Aristotle? GPT-3 made it possible,” founder McKay Wrigley tweeted in July.
And there aren’t just issues of validity to be considered. GPT-3 is a model trained on a breathless expanse of the internet, including nearly 500 billion words from Wikipedia, fan fiction and Reddit. As it has been repeatedly shown, the internet is rife with bias and discrimination — something that can get baked into automated systems.
Animashree Anandkumar, professor of computing at the California Institute of Technology and director of Machine Learning research at Nvidia, a computer graphics chip company, said that as a researcher working in AI, she wants to use these types of models for the benefit of humanity, and that that means rebuilding the foundations of the industry itself.
She noted that GPT-3’s use of parts of the internet to train its systems, such as Reddit, can introduce biases.
“It was certainly not a minority person who decided to use links from Reddits,” she said, adding that she has been threatened on the news aggregation site. She pointed out that a decision like that highlights the lack of diversity in the teams that build these technologies.
“There are already many examples of hiring apps discriminating against women and minorities based on indirect cues,” Anandkumar said. “If this is used as a way to generate text, it would only be generating certain types of gendered-language and not handle applications that require an unbiased approach to gender, race, religion and many other attributes.”
OpenAI declined to comment, but did address the potential harms and biases of its AI model in a blog post.
Anandkumar said that it can’t replace a human journalist because it doesn’t know the facts of the world, and cannot differentiate fact versus fiction. She said it will be a great source for fake news, and while in fiction it can be a fun way to see what the tool generates, there are still issues there.
“If you are constantly looking at stories GPT-3 generates and it’s gendered and it has sexism and racism, that furthers as a positive feedback loop,” she said.
“I worry if we are constantly exposed to AI models like GPT-3 that are biased, that further amplify the bias and continue to propagate it, what is the impact on human psychology?”
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