If you asked me 10 years ago, I would have said, "No way!" But if you ask me today, my answer would be more hesitant, "Not yet -- but perhaps someday soon." In particular, new "deep learning" artificial intelligence (AI) algorithms are showing promise in performing medical work which until recently was thought only capable of being done by human physicians. For example, deep learning algorithms have been able to diagnose the presence or absence of tuberculosis (TB) in chest x-ray images with astonishing accuracy. Researchers first "trained" the AIs with hundreds of x-ray images of patients without and with tuberculosis. Then, they tested the AIs with 150 new x-rays.

Biologic Intelligence is the natural, next generation of AI and is still being defined, but like with all new concepts, some people get it immediately, some people do not get it at all, and others get it somewhat but rely on their experiences with other concepts to try to make sense of what we are talking about. It's this latter group that we find is disheartening. We call it the Deep Learning Rut. Editor's Note: many people believe we aren't fans of, nor appreciate the value of, Deep Learning. It couldn't be further from the truth.

Machine learning and deep learning projects are gaining more and more importance in most enterprises. The complete process includes data preparation, building an analytic model and deploying it to production. This is an insights-action-loop which improves the analytic models continuously. Forrester calls the complete process and the platform behind it the Insights Platform. A key task when you want to build an appropriate analytic model using machine learning or deep learning techniques, is the integration and preparation of data sets from various sources like files, databases, big data storage, sensors or social networks. This step can take up to 80 percent of the whole analytics project. This article compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming ingestion and data wrangling.

Deep learning, coupled with Big Data is changing the Tech Industry.

Nvidia is on a quest to find the best "social impact" artificial intelligence startups as part of a program called Nvidia Inception, which is screening more than 600 entrants to cull the best AI startups in three big categories. We wrote about the first four candidates for the hottest emerging startup on Friday and the five most-disruptive startups on Sunday morning. And now we're focusing on the next five candidates in a category dubbed the "potential for social impact" startups. They all happen to be in the medical space. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank-style event last week to narrow down the competition. Huang and a panel of judges listened to pitches from 14 AI startups across three categories.

The following is a visualization of the neural network described in this blog post: Basic Python Network Here is the implementation of the network that this page uses: MultiLayer.ts For a simpler single layer network see here: SingleLayer.ts Both networks rely on only vanilla JavaScript and a simple matrix library I put together: MatrixUtil.ts The defaulted iteration count is 10 to prevent the page from executing too many calculations on first load. You should be able to get accurate results by setting the iteration count to 10000.

The idea of turning Canada into an AI Superpower came to life on a dock, in a lake, in Northern Ontario where a handful of entrepreneurs, industry leaders, and investors got together to discuss the long game for this country. Moving from a resource economy to one based on intellectual property. The recent announcement of Toronto's new Vector Institute for Artificial Intelligence is one of the most exciting and promising initiatives for the tech community in Canada. It's been paired with the recent federal budget that has dedicated significant funding to advancing innovation and research in this arena. We are home to world-class AI centres where the modern godfathers of deep learning – Geoffrey Hinton, Richard Sutton and Yoshua Benigo – are educating the next generation of scientists and engineers.

AI is making a huge comeback. It's fascinating to be part of an era where a machine (or a cluster of machines) can take on a chess champion or a Jeopardy contestant and be able to win those contests handily. The increased ease of availability of computing and huge amounts of data is helping immensely. In this seemingly futuristic battle of man versus machine, enterprises have realized that they are sitting on a wealth of data that has not been effectively used so far. Whether it's predicting buying patterns or detecting faults in consumer equipment in advance, it's clear that adapting AI techniques would yield a significant competitive advantage to enterprise solutions. The race for cognitive solutions has thus already begun. Are microservices really just "SOA done right"? Download this exclusive O'Reilly Report to find out. There are many reasons why enterprises are playing catch up. First and foremost, developers consider AI in the same realm as rocket science i.e. very hard to learn and with a significant learning curve. The traditional methods of software development break down, since a set of input(s) might yield different output(s) depending on other ambient factors, and it would be hard to do test driven development, for instance.

Aerin Kim, Data Scientist and Founder of resumé checker BYOR (Build Your Own Resume) uses Phrase2Vec NLP parsing technology to help users improve their CV by examining words and phrases and then, using the Deep Learning parser, suggesting how to make it better. Aerin will explain some Deep Learning NLP essentials at next weekend's AI With The Best online conference, a follow-up of her previous talk on Phrase2Vec which you can catch here. We were pleased to have asked Aerin lots of questions last time and happy to see the amazing progress for her startup! You can find out more during her live talk this weekend -- but for now, here are her answers to our burning questions. Q Congratulations on the growth of BYOR labs -- what have you been up to since last September?