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Use machine learning to find energy materials


The world needs more energy. Governments and companies are investing billions of dollars in technologies to harvest, convert and store power1. And as silicon solar cells approach the limit of their performance, researchers are looking to alternatives based on perovskites and quantum dots2. The batteries that store the energy must get cheaper, more efficient and longer-lasting3. And devices need to be manufactured from safe and abundant materials such as copper, nickel and carbon rather than from lead, platinum or gold.

The Risk of Machine-Learning Bias (and How to Prevent It)


As promising as machine-learning technology is, it can also be susceptible to unintended biases that require careful planning to avoid. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Many companies are turning to machine learning to review vast amounts of data, from evaluating credit for loan applications, to scanning legal contracts for errors, to looking through employee communications with customers to identify bad conduct. New tools allow developers to build and deploy machine-learning engines more easily than ever: Amazon Web Services Inc. recently launched a "machine learning in a box" offering called SageMaker, which non-engineers can leverage to build sophisticated machine-learning models, and Microsoft Azure's machine-learning platform, Machine Learning Studio, doesn't require coding. But while machine-learning algorithms enable companies to realize new efficiencies, they are as susceptible as any system to the "garbage in, garbage out" syndrome.

Data scientists at forefront of changes in technology businesses - Artificial Intelligence Online


For a field supposedly starved of talent, data science seems to have been minting a lot of new experts in a hurry. The depth of interest was on display this week in San Francisco, where 1,600 people turned up for a data science summit organised by Turi, a company run by University of Washington machine learning professor Carlos Guestrin. Mr Guestrin argues that all software applications will need inbuilt intelligence within five years, making data scientists -- people trained to analyse large bodies of information -- key workers in this emerging "cognitive" technology economy. Whether or not he is right about the coming ubiquity, there is already a core of critical applications that depend on machine learning, led by recommendation programmes, fraud detection systems, forecasting tools and applications for predicting customer behaviour. The adaptation of what was until recently the preserve of research scientists into production-grade business applications could point to a profound change in corporate competitiveness.

Derisking machine learning and artificial intelligence


The added risk brought on by the complexity of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks. Machine learning and artificial intelligence are set to transform the banking industry, using vast amounts of data to build models that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate value of more than $250 billion in the banking industry.1 1.For the purposes of this article machine learning is broadly defined to include algorithms that learn from data without being explicitly programmed, including, for example, random forests, boosted decision trees, support-vector machines, deep learning, and reinforcement learning. The definition includes both supervised and unsupervised algorithms. For a full primer on the applications of artificial intelligence, we refer the reader to "An executive's guide to AI."