Elizabeth Holmes founded her revolutionary blood diagnostics company, Theranos, when she was 19. It’s now worth more than $9 billion, and poised to change health care. ... In the fall of 2003, Elizabeth Holmes, a 19-year-old sophomore at Stanford, plopped herself down in the office of her chemical engineering professor, Channing Robertson, and said, “Let’s start a company.” ... Robertson, who had seen thousands of undergraduates over his 33-year teaching career, had known Holmes just more than a year. “I knew she was different,” Robertson told me in an interview. “The novelty of how she would view a complex technical problem–it was unique in my experience.” ... Holmes had then just spent the summer working in a lab at the Genome Institute in Singapore, a post she had been able to fill thanks to having learned Mandarin in her spare hours as a Houston teenager. Upon returning to Palo Alto, she showed Robertson a patent application she had just written. As a freshman, Holmes had taken Robertson’s seminar on advanced drug-delivery devices–things like patches, pills, and even a contact-lens-like film that secreted glaucoma medication–but now she had invented one the likes of which Robertson had never conceived. It was a wearable patch that, in addition to administering a drug, would monitor variables in the patient’s blood to see if the therapy was having the desired effect, and adjust the dosage accordingly. ... “I remember her saying, ‘And we could put a cellphone chip on it, and it could telemeter out to the doctor or the patient what was going on,’ ” Robertson recounts. “And I kind of kicked myself. I’d consulted in this area for 30 years, but I’d never said, here we make all these gizmos that measure, and all these systems that deliver, but I never brought the two together.” ... Still, he balked at seeing her start a company before finishing her degree. “I said, ‘Why do you want to do this?’ And she said, ‘Because systems like this could completely revolutionize how effective health care is delivered. And this is what I want to do. I don’t want to make an incremental change in some technology in my life. I want to create a whole new technology, and one that is aimed at helping humanity at all levels regardless of geography or ethnicity or age or gender.’ ” ... “Consumerizing this health care experience is a huge element of our mission,” Holmes says at our first meeting in April, “which is access to actionable information at the time it matters.” In our conversations over the next two months, she comes back to that phrase frequently. It is the theme that unifies what had seemed to me, at first, a succession of diverse, disparate aspects of her vision. ... Though she has now raised more than $400 million, she says she has retained control over more than 50% of the stock.
The attack raises two important questions for society. One is: Is he right about Herbalife being a pyramid scheme? That’s important because if he is, all but a handful of companies in the now $34.5 billion MLM industry, affecting 18 million distributors, are almost certainly pyramid schemes as well. ... The second and perhaps bigger question is: What if Ackman is wrong? One man’s dragon slayer is another man’s vigilante. Herbalife has had to spend almost $90 million defending against Ackman’s attack so far, according to its SEC filings, while its executives, employees, and distributors have all been villainized, if not defamed. While activist investing was already controversial, Ackman has taken it into new terrain. Is it sound public policy to have freelance, for-profit billionaire regulators roaming the landscape, no matter how well-intentioned? ... There is no federal statute defining “pyramid scheme.” For years MLM critics have begged the FTC to draw some bright-line rules—but in vain. Such schemes are usually prosecuted by the FTC as an “unfair or deceptive act or practice.” If an MLM or its distributors have merely made some misleading claims, the FTC may fine the company and let it live to see another day. But if the commission finds that an MLM is a pyramid scheme—which is considered inherently deceptive—it must shut it down. ... The best definition of pyramid scheme emerged from a 1975 case in which the FTC shuttered a cosmetics marketer called Koscot Interplanetary. The key feature is that a pyramid scheme pays its distributors rewards “for recruiting other participants into the program … which are unrelated to sale of the product to ultimate users.” ... Few MLMs are so foolish as to do that. Instead, they typically pay a distributor—as Herbalife does—based on the products he orders, and on the products ordered by his first three levels of recruits, i.e., his direct recruits, his recruits’ recruits, and his recruits’ recruits’ recruits. ... While judges and economists have proposed other definitions, most boil down to this: The more genuine a company’s product, and the more genuine the consumer demand for it, the less likely it is that the company is a pyramid scheme.
How the massive diesel fraud incinerated VW’s reputation—and will hobble the company for years to come. ... “Hoax,” of course, is a layman’s word. But plenty of legal terms also arguably apply, including “consumer fraud” and “false advertising.” They are fueling an explosion of litigation. That and the horrific reputational damage are subjecting Volkswagen to one of the severest challenges in its nearly 80-year history. ... The U.S. Department of Justice and the EPA have filed a civil suit that could theoretically subject VW to up to $45 billion in fines (though, in fairness, no one expects penalties quite that draconian). The DOJ and the EPA are also pursuing a criminal inquiry, as are prosecutors in Germany, France, Italy, Sweden, and South Korea. All 50 state attorneys general in the U.S. are also on the warpath, armed with state laws that, nominally at least, are every bit as crushing as the federal law. ... All of that comes on top of more than 500 class actions filed on behalf of owners and lessors of Volkswagen diesel cars ... VW’s misbehavior did not come out of nowhere. The company has a history of scandals and episodes in which it skirted the law. Each time—till now—it has escaped without dire consequences. ... VW is driven by a ruthless, overweening culture. Under Ferdinand Piëch and his successors, the company was run like an empire, with overwhelming control vested in a few hands, marked by a high-octane mix of ambition and arrogance—and micromanagement—all set against a volatile backdrop of epic family power plays, liaisons, and blood feuds. It’s a culture that mandated success at all costs.
Many companies already have the ability to run keyword searches of employees’ emails, looking for worrisome words and phrases like embezzle and I loathe this job. But the Stroz Friedberg software, called Scout, aspires to go a giant step further, detecting indirectly, through unconscious syntactic and grammatical clues, workers’ anger, financial or personal stress, and other tip-offs that an employee might be about to lose it. ... To measure employees’ disgruntlement, for instance, it uses an algorithm based on linguistic tells found to connote feelings of victimization, anger, and blame. ... It’s not illegal to be disgruntled. But today’s frustrated worker could engineer tomorrow’s hundred-million-dollar data breach. Scout is being marketed as a cutting-edge weapon in the growing arsenal that helps corporations combat “insider threat,” the phenomenon of employees going bad. Workers who commit fraud or embezzlement are one example, but so are “bad leavers”—employees or contractors who, when they depart, steal intellectual property or other confidential data, sabotage the information technology system, or threaten to do so unless they’re paid off. Workplace violence is a growing concern too. ... Though companies have long been arming themselves against cyberattack by external hackers, often presumed to come from distant lands like Russia and China, they’re increasingly realizing that many assaults are launched from within—by, say, the quiet guy down the hall whose contract wasn’t renewed.
The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above. In fact, no human could. Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data—hundreds of thousands of images or years’ worth of speech samples—to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences. ... Neural nets aren’t new. The concept dates back to the 1950s, and many of the key algorithmic breakthroughs occurred in the 1980s and 1990s. What’s changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data—images, video, audio, and text files strewn across the Internet—that, it turns out, are essential to making neural nets work well. ... That dramatic progress has sparked a burst of activity. Equity funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to the CB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch—with more than $6 billion of that coming since 2014. ... The hardware world is feeling the tremors. The increased computational power that is making all this possible derives not only from Moore’s law but also from the realization in the late 2000s that graphics processing units (GPUs) made by Nvidia—the powerful chips that were first designed to give gamers rich, 3D visual experiences—were 20 to 50 times more efficient than traditional central processing units (CPUs) for deep-learning computations. ... Think of deep learning as a subset of a subset. “Artificial intelligence” encompasses a vast range of technologies—like traditional logic and rules-based systems—that enable computers and robots to solve problems in ways that at least superficially resemble thinking. Within that realm is a smaller category called machine learning, which is the name for a whole toolbox of arcane but important mathematical techniques that enable computers to improve at performing tasks with experience. Finally, within machine learning is the smaller subcategory called deep learning.
- Also: FiveThirtyEight - Some Like It Bot < 5min
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- Also: Medium - Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning 5-15min
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