Robots will replace teachers by 2027. That's the bold claim that Anthony Seldon, a British education expert, made at the British Science Festival in September. Seldon may be the first to set such a specific deadline for the automation of education, but he's not the first to note technology's potential to replace human workers. Whether the "robots" take the form of artificially intelligent (AI) software programs or humanoid machines, research suggests that technology is poised to automate a huge proportion of jobs worldwide, disrupting the global economy and leaving millions unemployed. But just which jobs are on the chopping block is still a subject of debate.

The following problems are taken from the projects / assignments in the edX course Python for Data Science (UCSanDiagoX) and the coursera course Applied Machine Learning in Python (UMich). The IMDB Movie Dataset (MovieLens 20M) is used for the analysis. The dataset is downloaded from here . This dataset contains 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users and was released in 4/2015. Understand the trend in average ratings for different movie genres over years (from 1995 to 2015) and Correlation between the trends for different genres (8 different genres are considered: Animation, Comedy, Romance, Thriller, Horror, Sci-Fi and Musical).

Good advice on how NOT to be an academic when you finish your PhD is pretty thin on the ground. Many supervisors have never done anything else, and/or are not well enough connected with industry to know what is'hot'. Careers centres at universities tend to shape their offerings around the huge undergraduate cohort, who have very different needs. If you want to leave academia at the end of you PhD it's likely you will face some kind of career transition. While we train astrophysicists, we don't have any astrophysics companies in Australia.

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator. This paper considers the problem of finding a robust policy while taking into account the impact of environment variables. We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings. ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but play a substantial role in determining the optimal policy. Experimental results across different domains show that ALOQ can learn more efficiently and robustly than existing methods.

This article is devoted to the problem of predicting the value taken by a random permutation $\Sigma$, describing the preferences of an individual over a set of numbered items $\{1,\; \ldots,\; n\}$ say, based on the observation of an input/explanatory r.v. $X$ e.g. characteristics of the individual), when error is measured by the Kendall $\tau$ distance. In the probabilistic formulation of the 'Learning to Order' problem we propose, which extends the framework for statistical Kemeny ranking aggregation developped in \citet{CKS17}, this boils down to recovering conditional Kemeny medians of $\Sigma$ given $X$ from i.i.d. training examples $(X_1, \Sigma_1),\; \ldots,\; (X_N, \Sigma_N)$. For this reason, this statistical learning problem is referred to as \textit{ranking median regression} here. Our contribution is twofold. We first propose a probabilistic theory of ranking median regression: the set of optimal elements is characterized, the performance of empirical risk minimizers is investigated in this context and situations where fast learning rates can be achieved are also exhibited. Next we introduce the concept of local consensus/median, in order to derive efficient methods for ranking median regression. The major advantage of this local learning approach lies in its close connection with the widely studied Kemeny aggregation problem. From an algorithmic perspective, this permits to build predictive rules for ranking median regression by implementing efficient techniques for (approximate) Kemeny median computations at a local level in a tractable manner. In particular, versions of $k$-nearest neighbor and tree-based methods, tailored to ranking median regression, are investigated. Accuracy of piecewise constant ranking median regression rules is studied under a specific smoothness assumption for $\Sigma$'s conditional distribution given $X$.

We crafted a scalable, cost-effective approach for a new era of learning that puts the spotlight on --learning anytime, anywhere-- through digital technologies.-- With the Accenture Future Talent Platform, the client can now launch new services on its ecommerce site 75--percent faster than previously possible. The program will identify new roles and skills and build a training plan for a pilot, followed by a 40,000-person rollout. Accenture will also develop a curated, interactive curriculum for bank employees. Combining unmatched experience and specialized skills across more than 40 industries and all business functions -- underpinned by the world--s largest delivery network -- Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders.

We've spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived. Now, when major tech companies give their annual keynotes, they can't help but pepper the narrative with phrases like "machine learning." What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw? The future is here -- and it's equal parts exciting and terrifying.

Artificial intelligence (AI) is neither friend nor foe – It's both! This was the feedback received following a high level panel discussion held during the Science Forum South Africa (#SFSA2017) in Tshwane, were youth members from various African states exchanged their views and ideals as to how best they, business and government need to approach the digital age. "There is a paralysing fear that jobs will be taken away and humans will become redundant and lazy", said Barbara Glover of NEPAD Planning and Coordinating Agency. Glover added that these fears need to be allayed by understanding how best AI can unlock enhanced potential for humans to deploy their skills in new ways. Ama Duncan of Corporate Training Solutions in Ghana agreed.

Artificial intelligence (AI) is neither friend nor foe – It's both! This was the feedback received following a high level panel discussion held during the Science Forum South Africa (#SFSA2017) in Tshwane, were youth members from various African states exchanged their views and ideals as to how best they, business and government need to approach the digital age. "There is a paralysing fear that jobs will be taken away and humans will become redundant and lazy", said Barbara Glover of NEPAD Planning and Coordinating Agency. Glover added that these fears need to be allayed by understanding how best AI can unlock enhanced potential for humans to deploy their skills in new ways. Ama Duncan of Corporate Training Solutions in Ghana agreed.

I read with interest recent reports about China wanting to bring artificial intelligence (AI) to its classrooms to boost its education system. This brings us to the debate over whether artificial intelligence, that is, robots, will replace human teachers in the future. I believe that is unlikely. Of course, AI could help lighten the workload of classroom teachers, in areas like analysing students' past performance and optimising lessons accordingly; marking their tests and quizzes, and correcting their homework and assignments. Robot teachers could also deliver preprogrammed lessons and provide answers to frequently asked questions.