If these data sets aren't sufficiently broad, then companies can create AIs with biases. Speech recognition software with a data set that only contains people speaking in proper, stilted British English will have a hard time understanding the slang and diction of someone from an inner city in America. If everyone teaching computers to act like humans are men, then the machines will have a view of the world that's narrow by default and, through the curation of data sets, possibly biased.
FANUC, the world's largest maker of industrial robots, plans to start connecting 400,000 of their installed systems by the end of this year. The goal is to collect data about their operations and, through the use of deep learning, improve performance. Similarly, Kuka is building a deep-learning AI network for their industrial robots. FANUC is now moving forward to connect all its manufacturing robots. The system proactively detects and informs of a potential equipment or process problem before unexpected downtime occurs.
IBM (NYSE: IBM) today revealed a series of new servers designed to help propel cognitive workloads and to drive greater data center efficiency. Featuring a new chip, the Linux-based lineup incorporates innovations from the OpenPOWER community that deliver higher levels of performance and greater computing efficiency than available on any x86-based server. Collaboratively developed with some of the world's leading technology companies, the new Power Systems are uniquely designed to propel artificial intelligence, deep learning, high performance data analytics and other compute-heavy workloads, which can help businesses and cloud service providers save money on data center costs. The three new systems are an expansion of IBM's Linux server portfolio comprised of IBM's specialized line of servers co-developed with fellow members of the OpenPOWER Foundation. The new servers join the Power Systems LC lineup that is designed to outperform x86-based servers on a variety of data-intensive workloads.
Most viewed July stories Bayesian Machine Learning, Explained Why Big Data is in Trouble: They Forgot About Applied Statistics How to Start Learning Deep Learning Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey What Has Pokemon Got To Do With Big Data? 5 Big Data Projects You Can No Longer Overlook SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? Data Mining History: The Invention of Support Vector Machines Text Mining 101: Topic Modeling 5 Deep Learning Projects You Can No Longer Overlook Most shared Why Big Data is in Trouble: They Forgot About Applied Statistics Bayesian Machine Learning, Explained What Has Pokemon Got To Do With Big Data? Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? How to Start Learning Deep Learning Data Mining History: The Invention of Support Vector Machines 5 Big Data Projects You Can No Longer Overlook What is Softmax Regression and How is it Related to Logistic Regression? 7 Steps to Understanding NoSQL Databases
Rather than focus on attack signatures, these AI solutions look for anomalous network behavior, flagging when a machine goes rogue or if user activity or traffic patterns appear unusual. "A really simple example is someone with high privilege who attempts to get onto a system at a time of day or night that they never normally log in and potentially from a geolocation or a machine that they don't log in from," said Kelley. Another example would be a "really rapid transfer of a lot of data," especially if that data consists of the "corporate crown jewels." Such red-flags allow admins to quickly catch high-priority malware infections and network compromises before they can cause irreparable damage. IBM calls this kind of machine learning "cognitive with a little'c'" – which the company was already practicing prior to Watson.