Every day it seems we are hearing of new advances made by AIs thanks to Machine Learning, from improving healthcare to beating us at poker, it is often easy to forget that, behind every successful robot, there's a clever human. The swift pace of change we are seeing today is due to a concerted effort across industry and academia to find practical uses for the ever-growing amount of data we are generating and collecting. So, in this post I am going to highlight some of the current movers'n' shakers, whose breakthroughs in machine learning are proving to be fundamental to developing the digital tools and technologies making AI possible, from social networks to self-driving cars, to the industrial internet. Ng has just resigned from his post as chief data scientist at Chinese online giant Baidu. As well as that he is the founder of the online training resource Coursera and associate professor at Stanford University's computer science department.

There is much debate among scholars and practitioners about what data science is, and what it isn't. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics? One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

Every day it seems we are hearing of new advances made by AIs thanks to Machine Learning, from improving healthcare to beating us at poker, it is often easy to forget that, behind every successful robot, there's a clever human. The swift pace of change we are seeing today is due to a concerted effort across industry and academia to find practical uses for the ever-growing amount of data we are generating and collecting. So, in this post I am going to highlight some of the current movers'n' shakers, whose breakthroughs in machine learning are proving to be fundamental to developing the digital tools and technologies making AI possible, from social networks to self-driving cars, to the industrial internet. Ng has just resigned from his post as chief data scientist at Chinese online giant Baidu. As well as that he is the founder of the online training resource Coursera and associate professor at Stanford University's computer science department.

Training strategies have long since stopped being considered as'nice to have' motivational activity and more and more organisations are expecting close alignment of training and business in order to make training strategies effective. Some of the key expectations of the business from training include outcome driven approach, velocity in training delivery, adaptation to the dynamic needs of the business and tuning to the millennial mindsets in the design of the programme. In this context, it is prudent to take advantage of digital capabilities and design the strategy such that role-specific competency road map is built, which in turn is matched with the training modules that the employees are supported with. The HR Information Systems, Performance Management system and the Learning Management Systems should be integrated and provide the bedrock system for talent development for the organisation. The learning paths put in place for the employees should be supported with the right learning ecosystem both offline and online and be able to switch from one world to the other in a seamless fashion.

It's a few years old, but still acts as a great technical introduction to the topic.

Since SQL Server 2016, a new function called R Services has been introduced. Microsoft recently announced a preview for the next version of SQL Server, which extends the advanced analytical ability to Python. This new capability of running R or Python in-database at scale enables us to keep the analytics services close to the data and eliminates the burden of data movements. To get the most out of SQL server, knowing how to fine tune the intelligence model itself is far from sufficient and sometimes still fail to meet the performance requirement. There are quite a few optimization tips and tricks that could help us boost the performance significantly.

After a quick overview of how Google has utilized its Artificial Intelligence (AI) technology, the article states, "Some companies have built their own AI research units and need to build highly customized models for specific applications. Yet, in doing so they quickly run up against the immense hardware requirements of building large deep learning models, often requiring entire accelerator farms for rapid iteration. In Google's case it offers a hosted deep learning platform called Cloud Machine Learning Engine that takes care of the hardware needs of deep learning development, allowing companies to focus on building their models and offload the computing requirements to Google. After all, few companies have invested so much in AI that they have built their own custom accelerator hardware like Google did with its Tensor Processing Units (TPUs)." Further into the article, the author, Kalev Leetaru, states analytics companies "are interested in building services for their customers, not conducting AI research. In following its externalization trend, Google has risen to this challenge by releasing many of its internal AI systems as public cloud APIs."

How To Create a Website in a Weekend! (Project-Centered Course) State University of New York via Coursera (3 ratings) 8th May, 2017

A couple of years ago, Ashok Goel was overwhelmed by the number of questions his students were asking in his course on artificial intelligence. Goel teaches computer science at Georgia Tech, sometimes to large classes, where students can ask thousands of questions online in a discussion forum. With a limited number of teaching assistants, or TAs, many of those questions weren't getting answered in time. So, Goel came up with a plan: make an artificial intelligence "teaching assistant" that could answer some of students' frequently asked questions. In 2015 he built Jill Watson, his AI TA -- named after one of the IBM founders, Thomas J. Watson.

I was asked to write a short op-ed on the European Parliament Law Committee's recommendations on civil law rules for robotics. In the end, the piece didn't get published, so I am posting it here: It is a great shame that most reports of the European Parliament's Committee for Legal Affairs' vote on its Draft Report on Civil Law Rules on Robotics headlined on'personhood' for robots because the report has much else to commend it. Most important among its several recommendations is a proposed code of ethical conduct for roboticists, which explicitly asks designers to research and innovate responsibly. Some may wonder why such an invitation even needs to be made but, given that engineering and computer science education rarely includes classes on ethics (it should), it is really important that robotics engineers reflect on their ethical responsibilities to society – especially given how disruptive robot technologies are. This is not new – great frameworks for responsible research and innovation already exist.