AI experts gathered at MIT last week, with the aim of predicting the role artificial intelligence will play in the future of work. Will it be the enemy of the human worker? Will it prove to be a savior? Or will it be just another innovation--like electricity or the internet? As IEEE Spectrum previously reported, this conference ("AI and the Future of Work Congress"), held at MIT's Kresge Auditorium, offered sometimes pessimistic outlooks on the job- and industry-destroying path that AI and automation seems to be taking: Self-driving technology will put truck drivers out of work; smart law clerk algorithms will put paralegals out of work; robots will (continue to) put factory and warehouse workers out of work.

In late November, a college senior at Brigham Young University named Nick Walton published a short fable called "My Musical Troupe of Orcs Uses Music to Advance Orc Rights." In the story, written in the second person, you are a goblin. "I am a goblin!" you say proudly. "And I'm glad to be one." "Well then, congratulations," says the orc captain. Over the course of a few hundred words, some big things happen: You ask if you can join the orc band.

Kim Il Sung University set up specialist Japanese language and literature courses in the spring of 2017, it was learned Saturday from the university. The training course for Japanese researchers was established at the prestigious institution in Pyongyang, the capital of North Korea, at a time when the rogue state was repeatedly testing nuclear weapons and launching ballistic missiles. That period continued until the fall of 2017 and led to heightened tensions with the United States. There is a possibility that it was judged necessary to strengthen the development of such experts in view of future diplomacy with Japan. Japan and North Korea maintain no diplomatic relations.

Automated decision making comes of age.

But doing more with that data using machine learning is just what retailers need to really succeed in the current market. Machine Learning in Retail 5. 5 03 Data Science Applications …4 Skin Cancer Deduction Facial Recognition HR Analytics Recommendations 6. 6 04 Visual Analytics with Tableau 7. 7 05 Business Statistics Various Data Types a)discrete, b)continuous, c)Nominal, d) Ordinal, e) Interval Scale, f) Ratio • Central Tendency, • Measures of Dispersion • Random Variable • Probability Distribution • Normal Distribution • Skewness • Kurtosis • Random Sample • Confidence Interval • Sampling Frame • Z-Calculations • Central Limit Theorem • Chi-Square Test Analysis on X and Y data: One-Way Anova, 2 Sample t-test, Case Study on One-Way Anova and 2 Sample T Test,Hypothesis Testing Converting Statistics.. Hmm! 8. 8 06 Machine Learning Enabling machine to learn without being explicitly programmed 9. 9 DataMites is a global institute of Data Science, Machine Learning, IoT and Artificial Intelligence Training and Consulting for individuals and Corporate. For courses enquires Call: 1 415 8522477 (USA) 1800 200 6848 (India Toll Free) Email: enquiry@datamites.com But doing more with that data using machine learning is just what retailers need to really succeed in the current market. For courses enquires Call: 1 415 8522477 (USA) 1800 200 6848 (India Toll Free) Email: enquiry@datamites.com

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While it's perhaps prudent to take many of the doomsday predictions about the looming technological decimation of the labor market with a large pinch of salt, it is almost certain that whatever disruption does emerge will affect those in the most precarious position more than anyone. A recent report from the innovation group Nesta suggests that there are six million people in the U.K. who are in such a precarious position, and they caution that without assistance, these people will be stuck in a cycle of either low-pay and insecure employment or forced out of the workforce entirely. "The problem is that many people who are in low-paid work - or who aren't working at all - aren't able to access the information they need to plan for the future or the relevant training they need to gain new skills," the authors say. "They also tend to work in places and industries that are likely to lose out over the next decade, making it harder than ever for them to access good jobs." The challenge is compounded by the fact that those who are most at risk of disruption are also those least engaged with training and education.

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?

Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a learned policy. However, Bayesian IRL is computationally intractable for high-dimensional problems because each sample from the posterior requires solving an entire Markov Decision Process (MDP). While there exist non-Bayesian deep IRL methods, these methods typically infer point estimates of reward functions, precluding rigorous safety and uncertainty analysis. We propose Bayesian Reward Extrapolation (B-REX), a highly efficient, preference-based Bayesian reward learning algorithm that scales to high-dimensional, visual control tasks. Our approach uses successor feature representations and preferences over demonstrations to efficiently generate samples from the posterior distribution over the demonstrator's reward function without requiring an MDP solver. Using samples from the posterior, we demonstrate how to calculate high-confidence bounds on policy performance in the imitation learning setting, in which the ground-truth reward function is unknown. We evaluate our proposed approach on the task of learning to play Atari games via imitation learning from pixel inputs, with no access to the game score. We demonstrate that B-REX learns imitation policies that are competitive with a state-of-the-art deep imitation learning method that only learns a point estimate of the reward function. Furthermore, we demonstrate that samples from the posterior generated via B-REX can be used to compute high-confidence performance bounds for a variety of evaluation policies. We show that high-confidence performance bounds are useful for accurately ranking different evaluation policies when the reward function is unknown. We also demonstrate that high-confidence performance bounds may be useful for detecting reward hacking.

Our research is a step toward understanding when explanations of AIdriven hints and feedback are useful in Intelligent Tutoring Systems (ITS). We added an explanation functionality for the adaptive hints provided by the Adaptive CSP (ACSP) applet, an inte lligent interactive simulation that helps students learn an algorithm for constraint satisfaction problems. We present the design of the explanation functionality and the results of an exploratory study to evaluate how students use it, including an analysis of how students' experience with the explanation functionality is affected by several personality traits and abilities . Our results show a significant impact of a measure of curiosity and the Agreeableness personality trait and provide insight toward des igning personalized Explainable AI (XAI) for ITS .