Python Programming which is currently a trending programming language in the era of information technology, This general purpose programming language is not a newly developed programming instead it has been developed in the year 1991 by Guido Van Rossum. The main benefit of Python programming than other programming languages is it supports multiple programming paradigms which includes object oriented, procedural and functional programming language. Python programming language is also has been listed down in the Top Trending Technologies 2020 listed and also been ranked as the most used programming language last year. This article explains about the Python development trend in 2020 which will give you a clear idea of enhancing your career from learning Python. Artificial Intelligence is currently the hottest technology in the field of information technology, the main focus of artificial intelligence is to build human intelligence on machines using machine learning algorithms and problem solving techniques.

Various organizations keep floating data science platforms to simplify machine learning workflows. Besides, due to fierce competition in the market, oftentimes platforms keep replacing one another as and when it brings new capabilities to improve organizations' productivity. Built by the founder of Apache Spark, Databricks provides a unified analytics platform that allows data scientists to manage end-to-end machine learning workflows. The one-size-fits-all platform not only enables practitioners to explore, visualize and build superior machine learning models, but also allows them to scale it quickly with the help of collaboration. The platforms support a wide range of languages, IDEs and notebooks.

Creativity isn't the only route to discovery – automated analysis of huge amounts of data works, too. "Can machines think?", asked the famous mathematician, code breaker and computer scientist Alan Turing almost 70 years ago. Today, some experts have no doubt that artificial intelligence (AI) will soon be able to develop the kind of general intelligence that humans have. But others argue that machines will never measure up. Although AI can already outperform humans on certain tasks – just like calculators – they can't be taught human creativity.

The research revealed that while over half of organisations use artificial intelligence (AI) or machine learning in their security stack, nearly 60 percent are still more confident in cyberthreat findings verified by humans over AI. The survey responses, along with the theme of "Human Element" at RSA Conference 2020, reflect the need for security organisations to incorporate both AI- and human-centric offerings, especially in the application security space. Three-quarters of respondents use an application security tool, and more than 40 percent of those application security solutions use both AI-based and human-based verification. AI and machine learning have provided several advantages for cybersecurity professionals overall the past several years, especially in the face of the technology talent gap, which has left 45 percent of respondents' companies lacking a sufficiently staffed cybersecurity team. More than 70 percent of respondents agree that AI-based tools made their cybersecurity teams more efficient by eliminating over 55 percent of mundane tasks.

Trends and predictions in Artificial Intelligence can be difficult to predict, but one thing is certain - AI will make significant strides in the healthcare sector more so now given that we are battling a once in a lifetime pandemic. Although many businesses today throw the term "AI" around casually in their grey literature, it perfectly encapsulates today's technological zeitgeist, and it shows no signs of an exit. That's one prediction no one would hesitate to put their money on. But before we clear the fog for you and present my hot takes for AI in 2020, it'll be worth doing a run-down on its highlights over the past few years. We've seen AI breakthroughs in a wide variety of fields from healthcare to transportation.

Enterprises are increasingly pushed for faster insights from their ever-increasing data volumes. A TDWI senior analyst looks at how some vendors are responding. Accelerating speed to insight from data is critical to nearly all types of organizations, especially as managers seek to develop strategies for responding to unexpected and rapidly changing circumstances such as the global coronavirus outbreak. TDWI's recently published Best Practices Report, Faster Insights from Faster Data, takes an in-depth look at practice and technology issues that matter most in reducing delays in data life cycles and putting well-prepared and relevant data in the hands of users sooner. Not long after the publication of the report, I had the opportunity to visit with some technology providers exhibiting at the TDWI Las Vegas Conference and Strategy Summit in February. It was interesting to see how issues brought up in the report are being addressed by vendors.

Wired Magazine recently called for us to, post pandemic, "ditch our tech enabled tools of social distancing". But are our telepresence robots creating emotional distancing or are they actually improving our emotional lives. This week in our weekly "COVID-19, robots and us" discussion with experts, we're looking at the topic of virtual presence and emotional contact as well as many other practical ways that robotics can make a difference in pandemic times. Robin Murphy, Raytheon Professor at Texas A&M University and founder of the field of Rescue Robotics, was involved in the very first use of robots in a disaster scenario in 9/11. Since then she's been involved in multiple disaster responses worldwide, including the Ebola outbreak in 2014-2016.

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions. To decide on an action to take, CEM conducts a search for the action sequence with the highest return according to the dynamics model and reward. Action sequences are typically randomly sampled from an unconditional Gaussian distribution and evaluated on the environment. This distribution is iteratively updated towards action sequences with higher returns. However, this planning method can be very inefficient, especially for high-dimensional action spaces. An alternative line of approaches optimize action sequences directly via gradient descent, but are prone to local optima. We propose a method to solve this planning problem by interleaving CEM and gradient descent steps in optimizing the action sequence. Our experiments show faster convergence of the proposed hybrid approach, even for high-dimensional action spaces, avoidance of local minima, and better or equal performance to CEM. Code accompanying the paper is available here 1 .

In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations. Macro-Action Decentralized Partially Observable Markov Decision Processes (MacDec-POMDPs) provide a general framework for asynchronous decision making under uncertainty in fully cooperative multi-agent tasks. However, multi-agent deep reinforcement learning methods have only been developed for (synchronous) primitive-action problems. This paper proposes two Deep Q-Network (DQN) based methods for learning decentralized and centralized macro-action-value functions with novel macro-action trajectory replay buffers introduced for each case. Evaluations on benchmark problems and a larger domain demonstrate the advantage of learning with macro-actions over primitive-actions and the scalability of our approaches.

We study minimax convergence rates of nonparametric density estimation in the Huber contamination model, in which a proportion of the data comes from an unknown outlier distribution. We provide the first results for this problem under a large family of losses, called Besov integral probability metrics (IPMs), that includes $\mathcal{L}^p$, Wasserstein, Kolmogorov-Smirnov, and other common distances between probability distributions. Specifically, under a range of smoothness assumptions on the population and outlier distributions, we show that a re-scaled thresholding wavelet series estimator achieves minimax optimal convergence rates under a wide variety of losses. Finally, based on connections that have recently been shown between nonparametric density estimation under IPM losses and generative adversarial networks (GANs), we show that certain GAN architectures also achieve these minimax rates.