Researchers have overcome one of the major stumbling blocks in artificial intelligence with a program that can learn one task after another using skills it acquires on the way. Developed by Google's AI company, DeepMind, the program has taken on a range of different tasks and performed almost as well as a human. Crucially, and uniquely, the AI does not forget how it solved past problems, and uses the knowledge to tackle new ones. The AI is not capable of the general intelligence that humans draw on when they are faced with new challenges; its use of past lessons is more limited. But the work shows a way around a problem that had to be solved if researchers are ever to build so-called artificial general intelligence (AGI) machines that match human intelligence.

Dennis Anselmo was glad he used an Apple Watch -- it saved his life when it spotted a dangerous heart condition. A range of studies already exist that prove Apple Watch can identify heart problems, but when its data is coupled with deep learning systems it can achieve much more. Having already built the world's most accurate wearable heart monitor, Apple's sensor development teams must now be exploring new sensor technologies. There have been claims these include a non-invasive diabetes sensor. We know the company's commitment to the sector runs deep.

The National Grid has confirmed that it is in the "earliest stages" of discussions exploring the use of Artificial Intelligence (AI), which could potentially maximise the use of renewable energy by predicting peaks in demand across the UK. The National Grid, which operates and owns the infrastructure that transports electricity across the UK, has seen its ability in balancing and stabilising the grid challenged in recent years as intermittent renewables such as solar and wind have been fed into the energy mix. While the introduction of renewables into the mix forms a key role in both national and European legislation to decarbonise the grid, concerns have been raised as to the National Grid's ability to deal with fluctuating wind and solar resources, which can sometimes produce more energy than the system can cope with. Energy storage and demand response initiatives, whereby businesses either store surplus energy or increase or reduce energy consumption based on demand, are being incorporated by the National Grid, which is now "exploring what opportunities" AI could offer to balance the situation. The National Grid revealed that it is in discussions with the UK-based AI company DeepMind about introducing new technologies to help balance the grid and improve the use of renewables.

There is a lot of hype (as you know) related to Artificial Intelligence (AI), machine learning and specifically deep learning (complex neural networks). You also know (if you have been keeping up with the news) that we are all users of such techniques in many every day tools. But recently the technology has gotten a little too close for comfort. Some vendors in the data space, specifically focused on data quality, MDM and data management have started talking about how deep learning will change the use of those tools significantly. At this point, I am not so sure.

STRATA HADOOP WORLD SAN JOSE, Calif., March 14, 2017 – Cloudera, the provider of the leading global platform for machine learning and advanced analytics built on the latest open source technologies, today unveiled Cloudera Data Science Workbench, a new self-service tool for data science on Cloudera Enterprise which is currently in beta. Based on the company's acquisition of data science startup Sense.io last year, Data Science Workbench allows data scientists to use their favorite open source languages -- including R, Python, and Scala -- and libraries on a secure enterprise platform with native Apache Spark and Apache Hadoop integration, to accelerate analytics projects from exploration to production. "Cloudera is focused on improving the user experience for data science and engineering teams, in particular those who want to scale their analytics using Spark for data processing and machine learning," said Charles Zedlewski, senior vice president, Products at Cloudera. "The acquisition of Sense.io and its team provided a strong foundation, and Data Science Workbench now puts self-service data science at scale within reach for our customers." Cloudera Data Science Workbench's benefits include: Beyond the extensive Python and R ecosystems, as open data science expands to include deep learning frameworks like Tensorflow, Microsoft Cognitive Toolkit, MXnet, BigDL, and more, data science teams are looking for ways to bring these tools to their data, which is increasingly stored in Hadoop environments Cloudera Data Science Workbench delivers a safe and secure environment to combine the latest open source innovations with the unified platform Cloudera customers trust.

Want to learn about deep learning neural networks, or simply want to try and crash an AI-powered car? You can do both with the "Self-Driving Cars In A Browser" project. German software engineer Jan Hünermann watches two autonomous cars -- one colored pink, the other turquoise -- race around a track. There are various obstacles set up to confound them, but thanks to the brain-inspired neural networks that provide them with their intelligence, the cars smoothly navigate these obstacles with the confidence of seasoned pros. From time to time, Hünermann throws a new obstacle in their path, and then watches with satisfaction as the cars dodge this new impediment.

Google has successfully applied deep learning artificial intelligence algorithms to the diagnosis of breast cancer. In a study carried out by researchers taking part in Google's Brain Residency Program – a 12-month educational course in machine and deep learning – an algorithm was trained to detect breast cancer tumours in a dataset of digitised pathology slides provided by Dutch medical institute the Radboud University Medical Center. After'training' the algorithm, researchers were able to achieve a 92% sensitivity in picking out tumour cells from the slides – significantly higher than the 73% achieved by trained pathologists with no time constraint. In addition, the team recreated the accuracy in different datasets taken from other hospitals and scanning machinery. The team did report an average of eight false positive per slide compared to none from trained pathologists.

In the course of our work with Intel's data science and artificial intelligence initiatives, we often encounter people who are excited about the potential of artificial intelligence, and eager to learn more about the things Intel is doing to drive the industry forward. In many cases, these people have read about Intel's focus on AI, and now they are asking how they can get more involved in this forward-looking field. They often ask how they can advance their data science careers in the direction of AI. In Part 1 of this blog series, we talked about steps organizations can take to cultivate in-house expertise in advanced analytics and data science. In this second part of the post, we will take things down to a more personal level, and talk about steps individuals can take to chart a future that involves creating AI solutions.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is applied in various fields such as computer vision, speech recognition, NLP, web search, biotech, risk management, cyber security, and many others.

It's almost impossible to escape the impact frontier technologies are having on everyday life. At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning. These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution – more specifically, AI is the new industrial revolution. The most exciting and promising of these frontier technologies is the advancements happening in the deep learning space. While still nascent, it's deep learning percolating into your smartphone, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and helping us find solutions to climate change.