Technology leaders are looking to bring artificial intelligence out of its infancy to make breakthroughs in cognitive solutions. IBM Research announced it is teaming up with the Department of Brain and Cognitive Sciences (BCS) at MIT to accelerate the development of machine vision. Together, the organizations will make up the IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension (BM3C). BM3C is a multi-year collaboration to develop cognitive computing systems that resemble how humans understand audio and visual information. BM3C researchers will look into pattern recognition and prediction methods, as well as next-generation models to advance machine vision.

Microsoft on Tuesday announced artificial intelligence initiatives specifically targeting cancer. Such work puts the software giant in a supercomputing realm with rivals IBM and Google. Microsoft said its new initiative includes four research teams. The first is harnessing machine learning and natural language processing to help oncologists glean existing research data to better understand personalized care. A second team is pairing machine learning with what Microsoft called "computer vision" in work with radiologists to track tumor progression.

DESCRIPTION Amazon is building a European Machine Learning (ML) team in Berlin! The Machine Learning team at Amazon is comprised of technical leaders who develop planet-scale platforms for machine learning on the cloud, assist the benchmarking and future development of existing machine learning applications across Amazon, and help develop novel and infinitely-scalable applications that optimize Amazons systems using cutting edge quantitative techniques. The ML team innovates algorithms to drive automated decisions at scale in all corners of the company, including our eCommerce site and subsidiaries, Amazon Web Services, Seller & Buyer Services and Digital Media including Kindle. Being part of the Machine Learning team at Amazon is one of the most exciting job opportunities in the world today. If you are deeply technical, know how to deliver, highly innovative and long for the opportunity to build solutions to challenging problems that directly affect millions of people: there may be no better place than Amazon for you to impact the world!

How can you get better performance from your deep learning model? It is one of the most common questions I get asked. What can I do if my neural network performs poorly? I often reply with "I don't know exactly, but I have lots of ideas." Then I proceed to list out all of the ideas I can think of that might give a lift in performance. Rather than write out that list again, I've decided to put all of my ideas into this post. The ideas won't just help you with deep learning, but really any machine learning algorithm. How To Improve Deep Learning Performance Photo by Pedro Ribeiro Simões, some rights reserved. This list of ideas is not complete but it is a great start.

IBM Research have announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BMC3 collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

Do you know that some of the content you read wasn't written by human beings? Automated Insights states, its software created one billion stories last year, many with no human intervention. This content you are reading was written by a real person, but just think, what are the chances you haven't consumed that type of content without knowing it? So what does it mean for content marketing? Forbes contributor Jayson DeMers has a futuristic approach to that question: "Right now, human writers are available at a variety of expertise levels and costs, making it possible for any company to find a good fit for their own content needs. In the not-too-distant future, algorithms will outperform all of them, and at a far lower cost. Essentially, human writers will be completely out of the equation."

Computer scientists have developed a new technique to measure human emotions wirelessly. CBC Radio technology columnist Dan Misener explains how. In the past, this has been done with electrocardiography (ECG) monitors -- electrodes strapped to the body. But a team at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has built a device that can do this without wires. They call it EQ-Radio, and in a lot of ways, the device is similar to a Wi-Fi router.

If you believe everything you read, you are probably quite worried about the prospect of a superintelligent, killer AI. The Guardian, a British newspaper, warned recently that "we're like children playing with a bomb," and a recent Newsweek headline reads, "Artificial Intelligence Is Coming, and It Could Wipe Us Out." Numerous such headlines, fueled by comments from as the likes of Elon Musk and Stephen Hawking, are strongly influenced by the work of one man: professor Nick Bostrom, author of the philosophical treatise Superintelligence: Paths, Dangers, and Strategies. Bostrom is an Oxford philosopher, but quantitative assessment of risks is the province of actuarial science. He may be dubbed the world's first prominent "actuarial philosopher," though the term seems an oxymoron given that philosophy is an arena for conceptual arguments, and risk assessment is a data-driven statistical exercise. So what do the data say?

Cancer is like a computer virus and can be'solved' by cracking the code, according to Microsoft. The computer software company says its researchers are using artificial intelligence in a new healthcare initiative to target cancerous cells and eliminate the disease. One of the projects within this new healthcare enterprise involves utilizing machine learning and natural language processing to help lead researchers sift through all the research data available and come up with a treatment plan for individual cancer patients. IBM is working on something similar using a program called Watson Oncology, which analyzes patient health info against research data. Other Microsoft healthcare initiatives involve computer vision in radiology to note the progress of tumors over time and a project which Microsoft refers to as its "moonshot" aims to program biology like we program computers using code.

The release continues, "AI is creating new ways for people to engage with technology and with one another. Apple's Siri leverages natural language processing to recognize voice commands. Facebook's deep learning facial recognition algorithm can instantly identify a person with nearly 98 percent accuracy. And Amazon, Netflix and Spotify all utilize machine learning to understand how each item in their massive catalogs relates to the other and each customer's preferences. However, the technical expertise and infrastructure required to develop AI solutions are beyond the reach of most companies. They must bring together massive and diverse data sets, which requires significant engineering resources to manage complex data integration processes. Specialized predictive models must then be built to extract value from the data and continuously learn from it, requiring extensive data science expertise."