Globally, food waste is a massive problem. According to the Food and Agriculture Organization of the United Nations, around 1.3 billion tonnes of food is wasted globally every year. That's believed to be enough to feed the world's 815 million hungry people, four times over. But thanks to advancements in technology, this problem could one day be eradicated. Grocery technology pioneer Ocado, for example, has been able to slash food wastage rates to just 1 in 6,000 items by using data analytics, machine learning and artificial intelligence to manage its produce.

In the consumer realm, technologies based on artificial intelligence (AI) are slowly changing the way we manage everyday tasks. Take the driving app Waze, for example. Waze uses crowdsourced data, social networking conversations, and cognitive learning to help shave time off daily commutes by providing the most efficient route based on current conditions and individual driving preferences. Or consider products like Nest. Gone are the days of paying to heat or cool your house while no one's home.

Philips Healthcare has made a name for itself in the health IT market as a medical imaging device manufacturer. But Joseph Frassica, M.D., chief medical officer at Philips North America and head of Philips Research Americas, said the company is much more than that. "In the past, we've been thought of as a company that makes things, devices," Frassica said. "These days, about 60% of our research and development is focused on software, artificial intelligence, machine learning." Case in point: Philips Research North America, part of the company's global research organization and headquartered in Cambridge, Mass., has more than 450 employees working to develop and test cutting-edge products for the health IT market.

Kartik Hosanagar A Human's Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control (Viking, 2019) This is an odd moment in the history of technology and innovation. Technology companies have never been more powerful or influential. The five most valuable corporations in the world are all American tech giants, and the products they make and the services they provide continue to colonize an ever-larger chunk of our daily lives. Yet that very power has occasioned a serious anti-tech backlash, driven in part by a sense that these companies have too often exercised their might in a cavalier and careless fashion, and in part by anxieties about how their dominance may be hindering innovation. So it's only fitting that this year's best business books on technology and innovation and grapple with the fundamental challenges facing the tech world today -- how to continue to drive radical innovation, how to manage the rise of ubiquitous machine intelligence, and how to make software that's socially useful and beneficial as well as lucrative.

In the spring of last year, cybersecurity researcher Takeshi Sugawara walked into the lab of Kevin Fu, a professor he was visiting at the University of Michigan. He wanted to show off a strange trick he'd discovered. Sugawara pointed a high-powered laser at the microphone of his iPad--all inside of a black metal box, to avoid burning or blinding anyone--and had Fu put on a pair of earbuds to listen to the sound the iPad's mic picked up. As Sugawara varied the laser's intensity over time in the shape of a sine wave, fluctuating at about 1,000 times a second, Fu picked up a distinct high-pitched tone. The iPad's microphone had inexplicably converted the laser's light into an electrical signal, just as it would with sound.

Artificial intelligence can do some amazing things, but it's not perfect. Research scientist Dr. Janelle Shane has been cataloging "the sometimes hilarious, sometimes unsettling ways that algorithms get things wrong" on her website, AI Weirdness, and dives deeper into the topic in her new book, out this week. Time and time again, Dr. Shane's neural nets ingest the data she throws at them and spits out some strange stuff--from inedible recipes (horseradish brownies, anyone?) to bizarre cat names and paint colors from hell. At first glance, Dr. Shane's book--You Look Like a Thing and I Love You: How AI Works and Why It's Making the World a Weirder Place--seems like a lighthearted, cartoon-enhanced look at AI, but there are some lessons about human vulnerabilities. We spoke to Dr. Shane to find out why she wrote the book and what she hopes we'll learn from it.

In my last column, I humbly welcomed our robot lawyer overlords. After it was published, a number of people called me out on social media and chastised me for joining sides with the robots so willingly. It would seem that they were decidedly unfamiliar with the well-known meme to which I referred. Well, rest assured dear readers, I have every intention to resist any and all invading robot overlords unless and until I feel that resistance will be futile, at which point I plan to blindly welcome them. And, judging by the results of two recent technology surveys, my fellow lawyers are in my corner when it comes to resisting the robot lawyers who've come to steal their jobs.

In my last column, I humbly welcomed our robot lawyer overlords. After it was published, a number of people called me out on social media and chastised me for joining sides with the robots so willingly. It would seem that they were decidedly unfamiliar with the well-known meme to which I referred. Well, rest assured dear readers, I have every intention to resist any and all invading robot overlords unless and until I feel that resistance will be futile, at which point I plan to blindly welcome them. And, judging by the results of two recent technology surveys, my fellow lawyers are in my corner when it comes to resisting the robot lawyers who've come to steal their jobs.

Despite the proliferation of accelerator chips like Google's tensor processing unit (TPU) and Intel's forthcoming Nervana NNP-T, most machine learning practitioners are limited by budget or design to commodity processors. Unfortunately, these processors tend to train sophisticated AI models rather slowly, exacerbating one of the many challenges involved in AI R&D. MIT Computer Science and Artificial Intelligence Lab research scientist Alex Matveev and professor Nir Shavit cofounded the Somerville, Massachusetts-based startup in 2018, inspired by their work in high-performance multicore execution engines for machine learning. The pair describes Neural Magic as a "no-hardware AI company," in essence -- one whose software processes workloads on processors at speeds equivalent to (or better than) specialized hardware. Investors are impressed with what they've seen, evidently.

Dual use and responsible research: ethical challenges' took place at the Karolinska Institute in Stockholm from the 14th to the 17th of November 2018. This workshop showcased the interdisciplinary nature of not only the HBP itself, but the dual use of brain science and the societal impacts this may have. Hence, the purpose of the workshop was to offer a space for discussing both the disciplinary aspects of the HBP, such as neuroscience and medicine, and the wider interdisciplinary aspects such as dual-use and responsible research and innovation (RRI). In order to engage as wide a range of students and researchers as possible with these topics, the workshop was open to all. With lectures covering topics such as the fascinating chemistry behind drug addiction and the revolutionary technology CRISPR that enables geneticists and medical researchers to edit parts of a genome, the interest in the workshop was high.