Immune Engineering: Genetically engineered immune cells are saving the lives of cancer patients. That may be just the start.
Precise Gene Editing in Plants: CRISPR offers an easy, exact way to alter genes to create traits such as disease resistance and drought tolerance.
Conversational Interfaces: Powerful speech technology from China’s leading Internet company makes it much easier to use a smartphone.
Reusable Rockets: Rockets typically are destroyed on their maiden voyage. But now they can make an upright landing and be refueled for another trip, setting the stage for a new era in spaceflight.
Robots That Teach Each Other: What if robots could figure out more things on their own and share that knowledge among themselves?
DNA App Store: An online store for information about your genes will make it cheap and easy to learn more about your health risks and predispositions.
SolarCity’s Gigafactory: A $750 million solar facility in Buffalo will produce a gigawatt of high-efficiency solar panels per year and make the technology far more attractive to homeowners.
Slack: A service built for the era of mobile phones and short text messages is changing the workplace.
Tesla Autopilot: The electric-vehicle maker sent its cars a software update that suddenly made autonomous driving a reality.
Power from the Air: Internet devices powered by Wi-Fi and other telecommunications signals will make small computers and sensors more pervasive.

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.