Roy Harold Scherer Jr worked as a truck driver on the long haul to the top of his chosen profession. He later found film stardom under the name of Rock Hudson. Michael Dell, founder of US company Dell Computers, washed plates and was a waiter in Chinese and Mexican restaurants before he landed on a career in technology. Such humdrum tasks once allowed ambitious people to earn cash en route to the top. For others, they were full-time jobs.

In May 2015, Stanford PhD student Andrej Karpathy wrote a blog post entitled The Unreasonable Effectiveness of Recurrent Neural Networks and released a code repository called Char-RNN. Both received quite a lot of attention from the machine learning community in the months that followed, spurring commentary and a number of response posts from other researchers. I remember reading these posts early last summer. Initially, I was somewhat underwhelmed--as at least one commentator pointed out, much of the generated text that Karpathy chose to highlight did not seem much better than results one might expect from high order character-level Markov chains. Here is a snippet of Karpathy's Char-RNN generated Shakespeare: And without access to affordable GPUs for training recurrent neural networks, I continued to experiment with Markov chains, generative grammars, template systems, and other ML-free solutions for generating text.

Deepdetect is a young open source deep-learning server and API designed to help in bridging the gap toward machine learning as a commodity. It originates from a series of applications built for a handful of large corporations and small startups. It has support for Caffe, one of the most appreciated libraries for deep learning, and it easily connects to a range of sources and sinks. This enables deep learning to fit into existing stacks and applications with reduced effort. Machine learning is the next expected commodity on the developer's stack.

But what if you are not one of these? I received the following job ad in my mailbox (see below in italics), from a third-party recruiter, and it's probably for a data science position at Nike near Portland, Oregon (my guess). Basically, it's a 6-month gig to build an A/B platform. I discuss here a few aspects that make this job ad unlikely to attract talent, as well as remedies. This skills mix is not found in typical employees.

Have you ever begun a Google search, only to click on the words the box lays before you? Tagged a friend's face when Facebook prompted it? Have you spoken to your iPhone? The artificial intelligence technology behind these tools is neither self-aware nor homicidal. But they are driven by a computational technique called machine learning, which is, at its simplest, a way to teach machines to teach themselves.

"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.

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.

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.

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.

"It shows that people respond to robots in a primitive, social way," researcher Jamy Li said in the release. "Social conventions regarding touching someone else's private parts apply to a robot's body parts as well. This research has implications for both robot design and theory of artificial systems." As shown in the video above, the robot in the experiment also provides an anatomical definition of each body part touched. There were four female volunteers and six male volunteers in the small study, according to The Guardian.