A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry. The more I think about the recent breakthroughs in machine learning and deep learning algorithms, the more I think we are finally heading toward a smarter software future. Our smartphones are really not that smart when it comes to the intelligence equation. That is about to change. For years, I had been writing about the need for better predictive intelligence in our software.

Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Dr. Arno Candel, Chief Architect, H20.ai In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will show live demos of how to train sophisticated machine learning models on large datasets to solve common business problems. He will show how data scientists and application developers can use modern software tools to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech and marketing.

Deep learning, a new and growing area of machine learning, is widely regarded as an important step forward on the path toward true artificial intelligence. Tech firms such as Facebook and Google, as well as companies like Bloomberg (which focuses on financial technology and information), are already beginning to incorporate the technology into their product development. And while the exact definition of deep learning is still somewhat fluid, opportunities are growing for experts who can apply the technology to everything from speech recognition to bioinformatics, drug discovery and equities trading. The global analytics firm SAS says deep learning trains computers to perform human-like tasks such as recognizing speech, identifying images or making predictions. "Deep learning can break patterns into sub-components and build models very accurately," Wayne Thompson, the chief data scientist of SAS Data Science Technologies in Cary, N.C., explained.

Theano has more code available online in general (all though the amount of Tensorflow code/tutorials is rapidly increasing), if you learn one you should be able to pick up the other without much hassle.

The SaaS Co. is a Berlin-made company that scales and executes sales for B2B SaaS products with the help of deep learning. At TSC we find, contact, qualify, and set appointments with decision makers in order to hand them over to you. In this lecture-workshop-breakfast, you will learn how to win as a customer, enterprises like Microsoft, or startups like Twilio. On the other hand we will show you how to use Artificial Intelligence in a practical way in your daily sales processes.

DeepMind is an artificial intelligence lab in London that creates what are known as general purpose self-learning algorithms. The company, acquired by Google in 2014 for a reported 400 million, is best-known for creating software "agents" that have mastered games like Go and Space Invaders but it also wants to apply its technology to healthcare. Mustafa Suleyman, DeepMind cofounder and head of DeepMind Health, gave a talk at the King's Fund in London this week where he explained how the company is working with the NHS and what kind of benefits patients can expect to see in the long run. The company operates independently of Google and creates software that can think for itself. In order to create this kind of AI software, DeepMind draws on huge data sets that can help to teach DeepMind's AI how to perform certain tasks.

To compare where the humans and machines looked, the researchers created "attention" heat maps that could be laid over one another. On a scale of 0 to 1, where 0 is no overlap at all and 1 is complete overlap, the researchers found that the attention maps from the humans lined up at a rate of 0.63. But when comparing humans to machines, this figure was just 0.26. Explaining this difference is tricky. In one question in the study, for example, the humans and neural networks where shown a picture of a bedroom, and asked: "What is covering the windows?"

Highlights: VR, TensorFlow, Google Awareness APIs, Wide & Deep Learning, GitHub on BigQuery, Google Cast SDK, Google Maps Android API, AdMob Campaigns. The Developer Show is where you can stay up to date on all the latest Google Developer news, straight from the experts. New Google Cast SDK released for Android and iOS: goo.gl/TBlO05

When I attended translation courses, I was assigned to write a commentary on George F. Will's column Reading, Writing and Rationality on the Newsweek issue of March 17, 1986. Even then, with no Internet, and television as the dominant media, students were urged to read. That day, green activists were giving a demonstration of solar energy applications in a public park near the school, and our professor opened his lesson with a witty comment about the experiment he had witnessed during his lunch break. The history of innovation is full of inventors and manufacturers unable to understand the impact and actual use of their own work. Similarly, most innovations do not necessarily use the most recent and sophisticated technology, with their makers showing an outstanding capacity of interpreting and accelerating the transformations that are already underway.