Artificial intelligence is one of the technologies with the most transformative potential in business. According to research by McKinsey, 70 per cent of companies are likely to have adopted at least one form of AI by 2030. This will contribute to an additional $13tr of global economic activity. Machine learning – a subset of artificial intelligence – enables machines to get better at executing tasks without human intervention, by finding patterns in data, and learning from their experience. It's no surprise, therefore, that there has been an explosion in the number of machine learning companies worldwide.

Understanding software development procedures in 2019 is vital. With many new programming languages approaching the matter, the way we build programs, machines and everything in between changes on a daily basis. With this in mind, a common denominator in software builds, especially for what concerns mobile apps, has been Artificial Intelligence. It's no secret that automation and machine code have both been incredibly big from a development and business perspective and their further simplification in order for them to be applied to a variety of pieces of software is at a groundbreaking state nowadays. Let's analyse the matter in more detail.

Researchers at Argonne National Laboratory are working on optimization models that use machine learning, a form of artificial intelligence, to simulate the electric system and the severity of various problems. In a region with 1,000 electric power assets, an outage of just three assets can produce nearly a billion scenarios of potential failure. Argonne researchers apply machine learning to inform more reliable grid planning and operations. The following article is part of a series on Argonne National Laboratory's efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines. How much electricity will you need tomorrow?

Although the concept of artificial intelligence has been around for centuries it wasn't until the 1950's where the true possibility of it was explored. A generation of scientists, mathematicians and philosophers all had the concept of AI but it wasn't until one British Polymath, Alan Turing, suggested that if humans use available information, as well as reason, to solve problems and make decisions -- then why can't machines do the same thing? Although Turing outlined machines and how to test their intelligence in his paper Computing Machinery and Intelligence in 1950 -- his findings did not advance. The main halt in growth was the problem of computers. Before any more growth could happen they needed to change fundamentally -- computers could execute commands, but they could not store them.

Infosys has dropped plans to sell automation software company Panaya after failing to find a buyer for a year. Instead, the software services giant has started investing in the subsidiary, foreseeing the efficiency of the company in AI and automation strategy. The Israel-based software company's products include change impact analysis, automated code remediation, collaborative test management and test-execution, and ALM acceleration. It runs on Amazon Elastic Compute Cloud. As per analysts, Panaya was a good acquisition but the asset was never used by Infosys after the buyout because of the corporate governance fight between the former CEO Vishal Sikka and NR Narayana Murthy, Infosys founder.

In 2017, Russian President Vladimir Putin famously stated that whoever becomes the leader in artificial intelligence "will become the ruler of the world." Most experts on technology and security would agree with Putin about the importance of AI, which will ultimately reshape healthcare, transportation, industry, national security, and more. Nevertheless, Moscow's recognition of AI's importance will not produce enough breakthroughs to obtain the technological edge that it so deeply desires. Russia will ultimately fail in its quest to become a leader in AI because of its inability to foster a culture of innovation. Russia's anxieties about competing in the information age are far from new. In 1983, then-Soviet Minister of Defense Nikolai Ogarkov lamented to the New York Times that in the United States, "small children -- even before they begin school -- play with computers….here

The randomness in Stochastic Gradient Descent (SGD) is considered to play a central role in the observed strong generalization capability of deep learning. In this work, we re-interpret the stochastic gradient of vanilla SGD as a matrix-vector product of the matrix of gradients and a random noise vector (namely multiplicative noise, M-Noise). Comparing to the existing theory that explains SGD using additive noise, the M-Noise helps establish a general case of SGD, namely Multiplicative SGD (M-SGD). The advantage of M-SGD is that it decouples noise from parameters, providing clear insights at the inherent randomness in SGD. Our analysis shows that 1) the M-SGD family, including the vanilla SGD, can be viewed as an minimizer with a data-dependent regularizer resemble of Rademacher complexity, which contributes to the implicit bias of M-SGD; 2) M-SGD holds a strong convergence to a continuous stochastic differential equation under the Gaussian noise assumption, ensuring the path-wise closeness of the discrete and continuous dynamics. For applications, based on M-SGD we design a fast algorithm to inject noise of different types (e.g., Gaussian and Bernoulli) into gradient descent. Based on the algorithm, we further demonstrate that M-SGD can approximate SGD with various noise types and recover the generalization performance, which reveals the potential of M-SGD to solve practical deep learning problems, e.g., large batch training with strong generalization performance. We have validated our observations on multiple practical deep learning scenarios.

Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge for machine learning algorithms and neurobiological models alike. Factored representations can enable flexible behaviour by abstracting away general aspects of a task from those prone to change, while nonparametric methods provide a principled way of using similarity to past experiences to guide current behaviour. Here we combine the successor representation (SR), that factors the value of actions into expected outcomes and corresponding rewards, with evaluating task similarity through nonparametric inference and clustering the space of rewards. The proposed algorithm improves SR's transfer capabilities by inverting a generative model over tasks, while also explaining important neurobiological signatures of place cell representation in the hippocampus. It dynamically samples from a flexible number of distinct SR maps while accumulating evidence about the current reward context, and outperforms competing algorithms in settings with both known and unsignalled rewards changes. It reproduces the "flickering" behaviour of hippocampal maps seen when rodents navigate to changing reward locations, and gives a quantitative account of trajectory-dependent hippocampal representations (so-called splitter cells) and their dynamics. We thus provide a novel algorithmic approach for multi-task learning, as well as a common normative framework that links together these different characteristics of the brain's spatial representation.

The formalism of anchor words has enabled the development of fast topic modeling algorithms with provable guarantees. In this paper, we introduce a protocol that allows users to interact with anchor words to build customized and interpretable topic models. Experimental evidence validating the usefulness of our approach is also presented.

Given a measurement graph $G= (V,E)$ and an unknown signal $r \in \mathbb{R}^n$, we investigate algorithms for recovering $r$ from pairwise measurements of the form $r_i - r_j$; $\{i,j\} \in E$. This problem arises in a variety of applications, such as ranking teams in sports data and time synchronization of distributed networks. Framed in the context of ranking, the task is to recover the ranking of $n$ teams (induced by $r$) given a small subset of noisy pairwise rank offsets. We propose a simple SVD-based algorithmic pipeline for both the problem of time synchronization and ranking. We provide a detailed theoretical analysis in terms of robustness against both sampling sparsity and noise perturbations with outliers, using results from matrix perturbation and random matrix theory. Our theoretical findings are complemented by a detailed set of numerical experiments on both synthetic and real data, showcasing the competitiveness of our proposed algorithms with other state-of-the-art methods.