Research Report


Next-Gen Technology Separates Digital Leaders From The Rest

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According to the SAP Center for Business Insight report "SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart," in collaboration with Oxford Economics, 97% of the 3,100 surveyed executives are not successful in realizing that digital vision. Big Data and analytics, the Internet of Things, and machine learning: a higher percentage of digital leaders are integrating these innovations into their core infrastructure. "For example, the SAP Digital Transformation Executive Study found that nearly 50% of top executives in the manufacturing industry see investment in digital skills and technology as the most important revenue growth driver in the next two years. For more insight on digital leaders, check out the SAP Center for Business Insight report, conducted in collaboration with Oxford Economics, "SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart."


AIs that learn from photos become sexist

Daily Mail

In the fourth example, the person pictured is labeled'woman' even though it is clearly a man because of sexist biases in the set that associate kitchens with women Researchers tested two of the largest collections of photos used to train image recognition AIs and discovered that sexism was rampant. However, they AIs associated men with stereotypically masculine activities like sports, hunting, and coaching, as well as objects sch as sporting equipment. 'For example, the activity cooking is over 33 percent more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68 percent at test time,' reads the paper, titled'Men Also Like Shopping,' which published as part of the 2017 Conference on Empirical Methods on Natural Language Processing. A user shared a photo depicting another scenario in which technology failed to detect darker skin, writing'reminds me of this failed beta test Princeton University conducted a word associate task with the algorithm GloVe, an unsupervised AI that uses online text to understand human language.


Three Questions To ID If Your Business is Ready for AI

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The truth is somewhere in-between; while it is unlikely to entirely eliminate many occupations over the next ten years, AI and machine learning will impact almost all industries, jobs and business to varying degrees. These systems dramatically improving performance, save time, and free up your expensive human talent to focus on strategic tasks. McKinsey estimates that 59 percent of all manufacturing activities could be automated, while a whopping 73 percent percent of the activities that food service workers perform have the potential for automation. Forrester Research expects "enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications," as "enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models."


New AI system can decode your brain signals

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BERLIN: Scientists have developed a new artificial intelligence system that can decode brain signals, an advance that may help severely paralysed patients communicate with their thoughts. Researchers from University Hospital Freiburg in Germany led by neuroscientist Tonio Ball showed how a self-learning algorithm decodes human brain signals that were measured by an electroencephalogram (EEG). The system could be used for early detection of epileptic seizures, communicating with severely paralysed patients or make automatic neurological diagnosis. "Our software is based on brain-inspired models that have proven to be most helpful to decode various natural signals such as phonetic sounds," said Robin Tibor Schirrmeister, University Hospital Freiburg.


Humanoid Robot Sweats to Keep Cool

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The University of Tokyo robot, called Kengoro, can send small amounts of water through porous metal bones to prevent its numerous motors from overheating. Developed by mechano-informatics professor Masayuki Inaba and his team at the University of Tokyo's Jouhou System Kougaku Laboratory, the humanoid robot contains 108 motors and a bunch of other necessary gear. The channels work something like "sweat glands" that send water through its uniquely porous frame. "Our concept was adding more functions to the frame, using it to transfer water, release heat and at the same time support forces."


Machine-learning algorithms can dramatically improve ability to predict suicide attempts

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After a meta-analysis, or a synthesis of the results in these published studies, they found that no single risk factor had clinical significance in predicting suicidal ideation, attempts or completion. The authors also found that the ability of researchers to find factors that predict suicidal thoughts and behaviors did not improve over the 50 years they surveyed, and that some of the most popular factors to study--including mood disorders, substance abuse and demographics--are some of the weakest predictors. "Few would expect hopelessness measured as an isolated trait-like factor to accurately predict suicide death over the course of a decade," the researchers write. Colin Walsh, an internist and data scientist at Vanderbilt University Medical Center, along with FSU's Franklin and Ribeiro, looked at millions of anonymized health records and compared 3,250 clear cases of nonfatal suicide attempts with a random group of patients.


Multilevel modeling: What it can and cannot do - Statistical Modeling, Causal Inference, and Social Science

@machinelearnbot

We now consider our model as an observational study of the effect of basements on home radon levels. The proportion of homes with basements varies by county (see Figure 1), but a regression model should address that lack of balance by estimating county and basement effects separately. The new group-level coefficient γ2 is estimated at .39 (with standard error .20), For the radon problem, the county-level basement proportion is difficult to interpret directly as a predictor, and we consider it a proxy for underlying variables (e.g., the type of soil prevalent in the county). As we have illustrated in this article, these effects cannot necessarily be interpreted causally for observational data, even if these data are a random sample from the population of interest.


Fintech Use Reaching 'Mass Adoption' Among Digital Consumers

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Leveraging digital technology, combined with personalized solutions, fintech firms are differentiating the customer banking experience. The ability to combine multiple financial services within singular platforms provides China a significant platform for fintech growth, which was apparent in the research. The EY study found that there is significant positive sentiment among consumers around fintech solutions, with estimated adoption expected to exceed 50% globally. In addition to potential joint ventures, acquisitions and investments into fintech firms, incumbent financial services firms can learn from or acquire the services of fintech firms to enhance existing offerings," The Digital Banking Report, The Challenger Bank Battlefield, provides insight into more than 30 fintech challenger banking organization globally.


Smart computers decode brain activity

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The aim of the research was to build upon studies that are showing how computer science and artificial intelligence can take brain research in new directions. The study demonstrates how a self-learning algorithm can decodes human brain signals, as measured by an electroencephalogram. Such research is directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. The aim of this was to further understand the diverse intersections between human and machine and to better develop artificial intelligence for medical science, in relation to interpreting brains scans.


Causation: The Why Beneath The What

@machinelearnbot

Causal inference literature in statistics, and in the biomedical and social sciences focus on what Aristotle called "efficient causes." We can try to predict actions, and possibly even reasons, but again the recent developments in causal inference literature in statistics and the biomedical and social sciences focus more on "efficient causes." Most of the work in the biomedical and social sciences on causal inference has focused on this sufficient condition of counterfactual dependence in thinking about causes. Some areas that might have exciting developments in the future include causal inference with network data, causal inference with spatial data, causal inference in the context of strategy and game theory, and the bringing together of causal inference and machine learning.