Researchers at the University of Texas at San Antonio revealed a novel learning platform for AI referred to as the “Artificial Intelligence Thinking”. AI Thinking is a cloud based framework that teaches computers on how to learn like humans which is a great step towards creating intelligent machines. The new AI platform provides personalised learning experience based on automatic cognitive feedback from the students and renders content accordingly to enhance the interest of students and increase the learning rate.
(Source : http://in.pcmag.com/artificial-intelligence/119553/news/researchers-develop-ai-platform-that-can-learn-like-humans )
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Harvard researchers have developed an efficient machine learning algorithm which can provide personalized control strategies for soft, wearable exosuits considerably improving the performance of the device. This new method is an effective and fast way to optimize control parameter settings for assistive wearable devices. Researchers have used the so-called human-in-the-loop optimization that uses real-time measurements of human physiological signals like breathing rate and adjusts the control parameters of the device. The algorithm directs the exosuit on when and where to deliver its assistive force to enhance hip extension. Using this method, they have seen a huge improvement in the metabolic performance for the wearers of a hip extension assistive device.
(Source : https://www.nextbigfuture.com/2018/03/harvard-uses-machine-learning-for-personalized-control-of-wearable-exosuits.html )
Organizations spend up to $75 billion on security every year but reports reveal that 2/3rd of organizations still experience a breach.Centrify provides identity and access management solutions with Zero trust security model as the major focus. According to Bill Mann, Centrify’s Chief Product Officer, security begins with identity. Centrify highlights the need for an identity and access management strategy as part of a Zero Trust Strategy for the reason that 80% of breaches in an organization arise from compromised credentials. A company can make the most out of big bucks by incorporating a next generation access strategy which verifies the user, authenticates the devices and imposes restrictions on limits access and privileges.Centrify’s solution is the best example of how machine learning can enhance the efficacy of a product while providing a more positive user experience to the organization's security admins and employees.
(Source - https://www.forbes.com/sites/maribellopez/2018/03/14/machine-learning-a-new-weapon-in-your-security-arsenal/#36c0fcdc36c2 )
Drug development requires years of research and costs millions of dollars before getting them ready for clinical trials. Many biotech startups like Reverie Labs are making use of machine learning to revolutionize the process and get drugs into pharmacies rapidly.Reverie Labs aims to speed up the process of identifying promising molecules with the use of latest machine learning algorithms.Riverie Labs is using the recently discovered machine learning techniques for drug development. In this technique, molecules are first featurized or converted into representations to work with machine learning algorithms. The tech team creates proprietary featurizations which are based on quantum chemical calculations and are used to analyse the molecules properties on how they may act in the body.Based on the results, the molecules that have the potential to do well in clinical trials are selected or new molecules are suggested based on the scientists requirements.
(Source : https://techcrunch.com/2018/03/16/reverie-labs-uses-new-machine-learning-algorithms-to-fix-drug-development-bottlenecks/ )
Machine Learning is changing the way you work, earn a livelihood, purchase and consume goods and services.Machine learning technology is growing at a rapid pace paving up increasing number of opportunities for working professionals at an exponential rate. 2018 is the best time for aspiring entrepreneurs, business owners and IT professionals to adapt machine learning , instead of being swept away by it. Machine learning patents were the third fastest growing category growing at 34% between 2013 and 2017 . IDC predicts that the spending on AI and Machine learning will grow from $12 billion in 2017 to $57.6 billion by 2021. The number of machine learning applications will double in 2018 compared to 2017.
(Source : https://yourstory.com/2018/03/machine-learning-buzzword/ )
Machines are smarter in making hiring processes more efficient. Machine learning adds value by processing job applications faster than human beings thereby reducing the time taken to recruit and hire new talent. Following are three important ways machine learning is helping organizations improve the hiring process -
i) Hiring managers need not dig through applications from hundreds of candidates manually to find the best fit. Machine learning provides intelligent recommendations to hiring managers on the candidates who can be an ideal fit for a given job roles. This makes the hiring process easier and efficient for both recruiters and job seekers.
ii) Machine learning helps eliminate bias as the algorithms emphasize on skill based data and not on the universities from where a candidate has studied or the companies they have worked for or gender.
iii) Machine learning can help create awareness around preferences of recruiters and hiring managers.
(Source : https://www.forbes.com/sites/forbestechcouncil/2018/03/26/three-ways-machine-learning-is-improving-the-hiring-process/#7ec5b2a790e8 )
With the data centers accounting for 2% of the global energy usage , maintaining efficiency across its individual network of 14 major hubs has always been a priority for Google. The complex nature of The complex nature of the equipment implies that there are billions of possible configurations of servers , chillers , cooling towers, heat exchangers and control systems. The metric used by Google to rate energy efficiency in data centers Power Usage Effectiveness (PUE) is complex for even highly trained Google data center engineers to work out amd optimize for. Google’s engineer Jim Gao turned to machine learning to optimize data center cooling. Machine learning is powerful tool to optimize cooling infrastructure in data centers, consider about 10 devices each having 10 settings , that’s probably 10 billion potential configurations something which humans cannot optimize but machine learning can.The result from machine learning has helped Jim and his team get a further 40% reduction in the overall amount of energy used for cooling the data center.
(Source : https://www.forbes.com/sites/bernardmarr/2018/03/28/machine-learning-at-google-the-amazing-use-case-of-becoming-a-fully-sustainable-business/#4a226a2759bc )