"Mistakes are the portals of discovery."- This is true in most cases, but in case of data scientists, making mistakes help them discover new data trends and find more patterns in the data. Having said this, it is imperative to understand that Data Scientists have a very small margin for error. Data Scientists are hired after a lot of deliberation and at a high cost. Organizations cannot afford to disregard bad data practices and repeated mistakes from Data Scientists.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

We identified 32 companies that are already applying machine learning techniques and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things. The 32 startups on the list have raised more than 530M in aggregate funding. This year, New York-based AiCure raised 12.3M in Series A funding and National Science Foundation-grantee Cloud Pharmaceuticals raised a 350K round from undisclosed investors. London-based health services startup, Babylon Health, raised a 25M Series A round from investors including Google-owned DeepMind Technologies and Hoxton Ventures. The company will reportedly roll out a Siri-like voice recognition interface this year.

When different disciplines such as music and technology come together, they can spark remarkably interesting ideas. Thus, it's no surprise to see that--for those of you who are aspiring musicians, avid guitar players, or anything in-between--there's a new program out there that promises to help you master the guitar. Allen Van Wert, who is known as one of the fastest guitar pickers in the world, developed an artificial intelligence program called the Ultimate Picking Program. Van Wert created the program to cut costs associated with private teachers and to customize lessons based on the user's skills. The first time you use the program, you complete a test that assesses your strengths and weaknesses.

Both are good examples of machine learning. Microsoft added a natural language processing layer, arguably pushing Tay into cognitive computing territory. But as far as true AI goes, neither comes close. In 1956 Herbert Simon, a Carnegie Mellon researcher who's considered to be one of the founding fathers of computer AI systems, said, "Machines will be capable, within 20 years, of doing any work a man can do." Leaving aside the wildly inaccurate timescale of his prediction, the quote serves as a good guide to what AI has always been about -- replacing human brain power with machine brain power.

IBM says it wants to make intelligent computers that can make decisions like humans. This week, it shipped the NS16e, its largest brain-inspired computer yet, and has big goals ahead. The company plans to create bigger versions of the NS16e -- which was purchased by Lawrence Livermore National Laboratory -- to come closer to matching the scale of a human brain. "Perhaps one day we may see a single rack of neurosynaptic system with as many neurons and synapses as in a human brain," said Jun Sawada, a researcher at IBM, in a blog entry. The brain can be viewed as an extremely power-efficient biological computer.

I've been doing a lot of research and reading on Artifical Intelligence (AI). What I find truly fascinating about the subject is it's moral and ethical implications on society as a whole. It's not too long ago that Microsoft introduced Tay to the world on Twitter, only to have Tay become a racist and sexist bot within 24 hours. She was only learning, after all, from her fellow humans. The internet can be a hostile place without any accountability whatsoever. As co-founder of an anonymous messaging app, I've witnessed the horrible things people say and do, and as a result -- I have very little hope for humanity.

Amazon's artificial intelligence ambitions are growing, with rumors surfacing today that it purchased an AI startup focusing on image processing. Amazon acquired Sunnyvale, Calif.-based Orbeus in the fall of 2015, according to an anonymous source that spoke with Bloomberg. In the past, Orbeus developed a neural network-based AI solution to categorize and identify photos. Orbeus previously offered the solution as a service for other developers under the name ReKognition, but Orbeus' website says the service is "no longer taking new customers." "But we're up to new/exciting things," the short note says.

In episode five of Season two Ryan walks us through variational inference, we put some listener questions about Go and how to play it to Andy Okun, president of the American Go Association (who is in Seoul South Korea watching the Lee Sedol/AlphaGo games). Plus we hear from Suchi Saria of Johns Hopkins about applying machine learning to understanding health care data.

At least, that's what we've been telling young professionals and mid-career workers alike who want to hack it in the modern workforce--in fact, it's advice I've given myself. And judging by the proliferation of coding schools and bootcamps we've seen over the past few years, not a few have eagerly heeded that instruction, thinking they're shoring up their livelihoods in the process. Unfortunately, many have already learned the hard way that even the best coding chops have their limits. More and more, "learn to code" is looking like bad advice. Anyone competent in languages such as Python, Java, or even web coding like HTML and CSS, is currently in high demand by businesses that are still just gearing up for the digital marketplace.