Natural Language Processing (NLP) has long been one of the holy grails of computer science. While we all know that computers are better than humans at making sense of highly structured information, there are still some important areas where humans are undeniably better than machines. Understanding language is one of those areas. For humans, understanding language is so natural we usually don't even have to make a conscious effort to do it. In reality, though, processing language and turning it into meaningful information is an extremely complex and difficult task.

Now seems like as good of a time as any to post some of the talks that I've done in the last year. I was originally going to give a talk I had done before (hence the same title), but I had some recent inspiration about some issues in the field that I wanted to share. This was a last minute whim, and after 24 hours of neither eating nor sleeping, I finished my slides with 30 minutes to spare. The talk went great and immediately afterwards, I even had the opportunity to do a TWiML podcast (thanks, Sam!). Both were surprisingly well-received, and writing the talk really helped me refine my thoughts on the field and what direction I wanted to take my work.

Imagine that instead of visiting a doctor for a skin abnormality, you could perform a self-examination with your phone, and artificial intelligence would diagnose you. They developed a machine-learning AI that taught itself to diagnose skin cancer the same way a human dermatologist would in an exam, and it worked. The researchers used one of Google's AI systems that was taught to identify different objects. They trained the algorithm on images of different skin ailments to help build a data bank that would be used to compare conditions. The algorithm was tested against dermatologists and it performed just as well.

As I start to look at the emerging trends of 2017 from the vantage of IndieBio, where we see hundreds of biotech startup applications and technologies per year, a few key themes are already emerging. Even as political landscapes change, science and technology continue to push forward. Most of us have seen science fiction shows that show future doctors regrowing and replacing entire organs. That fiction is now becoming a reality with cell therapies from companies like Juno (curing two infants with leukemia of their previously treatment resistant cancers with engineered T-cells), induced pluripotent stem cells (iPS) pioneered by the Nobel prize winning scientist, Shinya Yamanaka that can become any cell in the body, growing organoids (mini organs with some function of a fully grown organ like the stomach organoids grown by researchers in Ohio), and entirely re-grown organs. There are a plethora of biotechs focused on developing the next generation of regenerative therapies.

WIRE)--Join Rocket Fuel (NASDAQ: FUEL), a predictive marketing platform, throughout March, as the company participates in three top technology events in 2017: SXSW (March 10 - 15, 2017), IBM InterConnect (March 19 - 23, 2017), and IBM Amplify (March 20 - 22, 2017). Rocket Fuel will take the stage with its partners to share how its predictive marketing platform, combined with the power of AI and machine learning technologies, is changing the way brands and agencies connect with their customers. Have you been struggling to make sense of call transcripts, sales notes and other unstructured data to better understand what's really going on in your sales accounts, products, marketing campaigns and services? Watson Discovery Service can help ... without breaking the bank or the staffing plan. IBM Watson experts will discuss the latest advancements with the service that can enable you to quickly upload, enrich, and index large collections of your private data or public data.

One of the best parts of writing the Afternoon Edition each day is that I get to explore all kinds of interesting topics. Two of those recent topics continue to be all over the news, and they are the issues of robots and automation, and artificial intelligence and the "Singularity." These topics do materially affect markets, although their effect may not be as acute as, say, a monthly employment report. And speaking of which, we got a new monthly employment report this morning, as February saw 235,000 new non-farm payroll jobs created. That's good economic news; however, I wonder how many more jobs would have been created if there weren't as many advancements in the field of automation.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University collaborated to design a system that combines neuroscience and machine learning to create a mind-reading robot. This robot can read your mind to learn if it is right or wrong. According to Forbes, "The researchers created a system that allows a robot to correct its mistakes in real time when a human observer notices that a mistake is being made. The observer just sits there and watches; she doesn't physically interact with the robot or anything else. If the observer recognizes that a mistake is occurring, the robot changes course and does the right thing."

Phillip De Leon has been named associate dean of research and doctoral studies for the New Mexico State University College of Engineering. LAS CRUCES -- Through the use of New Mexico State University's High Performance Computing system, a supercomputer known as Joker, Phillip De Leon, associate dean for research in the College of Engineering, not only conducted research but students in his graduate electrical engineering course also used the system. In the Pattern Recognition and Machine Learning course, which is a data science class De Leon taught in the fall, graduate students used Joker on projects that included developing machine learning codes and evaluating the models with standard datasets. "These projects including identifying a song much like the Shazam app, recognizing handwritten digits like ZIP codes, classifying email as ham or spam, analyzing Twitter feeds, etc.," De Leon said. "Being able to use this system allowed the students to experiment and tune their codes much faster since everything ran much faster. It also allowed for big datasets to be used in training and evaluation."

Machine learning is a trending topic today and for good reason. It has enormous potential to transform entire markets and industries. Successful machine-learning startups will be the ones targeting vertical applications with a clear need for the technology. The consumer packaged goods industry is a good example. Machine learning can more accurately predict inventory levels to better manage the supply chain, reduce inventory costs, minimize excess capacity requirements, and eliminate stockouts.

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