Machine Learning is rarely sufficient, in isolation, to segment your target population into (for example) low risk and high risk groups, especially in highly imbalanced fraud-like problems. This is exacerbated when the emphasis is on identifying the negligible risk population, rather than the potential frauds. Following on from Lee Brown's blog, in this post I want to talk about how in an assurance scoring framework you can combine multiple data science techniques to identify the negligible risk cohort that can be fast tracked through processing, allowing investigators to focus their resources elsewhere. The figure below shows how these techniques can be chained together into an assurance scoring framework. Firstly, business rules can be used to solve a variety of problems, and also incorporate business users intuition. I've found that a successful approach is to think of the outcome of a machine learning classification algorithm as providing a high risk and a low risk bucket.

Using text mining and machine learning based on the SAP HANA platform, the initiative aims to help companies review job descriptions, performance reviews and similar people processes for potential bias and suggest changes to encourage equity. The announcement was made at the 28th annual SAPPHIRE NOW conference. These new capabilities will complement existing SAP SuccessFactors offerings that already help address inequity. Analytics and reports focused on diversity and inclusion are available to help organizations identify and track where biases exist in talent acquisition and management processes -- recruiting, compensation, succession and the like -- coupled with guidance on actions to take to address those biases. SAP is also exploring applications for mentoring programs that will help people from historically disadvantaged groups more effectively navigate and develop their careers, as well as tools for balancing family and work that will integrate elements of benefits, scheduling and management into a single process.

As Google announced at its I/O developer conference today, the company recently started building its own specialized chips to expedite the machine learning algorithms. These so-called Tensor Processing Units (TPU) are custom-built chips that Google has now been using in its own data centers for almost a year, as Google's senior VP for its technical infrastructure Urs Holzle noted in a press conference at I/O. Google says it's getting "an order of magnitude better-optimized performance per watt for machine learning" and argues that this is "roughly equivalent to fast-forwarding technology about seven years into the future." Google also manages to speed up the machine learning algorithms with the TPUs because it doesn't need the high-precision of standard CPUs and GPUs. Instead of 32-bit precision, the algorithms happily run with a reduced precision of 8 bits, so every transaction needs fewer transistors. If you are using Google's voice recognition services, your queries are already running on these TPUs today -- and if you're a developer, Google's Cloud Machine Learning services also run on these chips.

There are reasons to believe that true AI is right around the corner but I don't see it coming from the mainstream AI community. Right now, they are all having a feeding frenzy over a soon to be obsolete technology. There is no question that deep learning is a powerful and useful machine learning technique but it works in a narrow domain: the classification of labeled data. Someone has to go through the data and carefully label it according to a category or class. This is kind of lame because this is not the way humans and animals learn.

Developing effective tools against cancer has been a long, complicated endeavor with successes and disappointments. Despite all, cancer remains the leading cause of death worldwide. Now, machine learning and data analytics are being recruited as tools in the effort fight the disease and show significant promise according to two recent papers. In one paper – An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer – researchers from MIT and Stanford "propose models that use machine learning and optimization to suggest regimens to be tested in phase II and phase III trials." Their work, published in March in Management Science, could help cut costs and speed clinical trials.

There can be no doubt that technology trends over the years point to a rapid change in user requirements. The days of relying on a large, clunky desktop PC to provide a portal to the internet and other traditional, desktop-only applications are quickly diminishing. While PC sales continue to plummet, smartphone sales continue to soar and innovative devices such as Chromebooks are increasing their market share dramatically, poaching customers that, traditionally, would rely on desktop programs that can now be fully accessed in the cloud. The importance of speed and portability to users cannot be understated, but can cloud computing really enhance the data analytics sector? Their push into analytics with'Cloud Machine Learning' demonstrates their eagerness to challenge the offerings from their big-name competitors, such as IBM and Microsoft, entering an already crowded marketplace.

The CEO of Baltimore-based nonprofit Digit All Systems Inc., Lance Lucas described a real-world approach to using coding to create opportunity. His company has trained more than 10,000 students in cybersecurity certifications and computer programming through partnerships with 60 schools in the Baltimore/Washington metropolitan area, and has donated more than 3,500 computers to churches, schools, community groups and other organizations in need. He organized the first guns-for-computers trade-in program in the U.S. in 2013 and, after the rioting in Baltimore in 2015, offered people in affected areas a chance to reboot their lives.

Google has taken a big leap forward with the speed of its machine learning systems by creating its own custom chip that it's been using for over a year. The company was rumored to have been designing its own chip, based partly on job ads it posted in recent years. But until today it had kept the effort largely under wraps. It calls the chip a Tensor Processing Unit, or TPU, named after the TensorFlow software it uses for its machine learning programs. In a blog post, Google engineer Norm Jouppi refers to it as an accelerator chip, which means it speeds up a specific task.

During the keynote of its three-day developers' conference, Google CEO Sundar Pichai focused on Google's plans to bake artificial intelligence and machine learning more thoroughly into all of its services. In that vein, the company also unveiled a bunch of new products, including two messaging apps and a smart speaker.

Thousands of people gathered near Google's headquarters on Wednesday to hear the company's vision for the future. In past years, Google has used its developers conference to unveil all sorts of shiny new toys and services. Not all of them have been smash hits. Google Glass had its big coming out party at IO back in 2012, after all. Google TV was the star of 2010. And remember the Nexus Q, the orb-shaped music player that never even reached the market?