Considering its merits and flaws, it is worth asking if GitHub Copilot affects developer jobs in the future. When GPT-3 was released, the answer to this question was a tentative, faint yes. However, now that Copilot is out and will be a commercially available product that integrates into one of the heavily used IDEs globally, we should reconsider our answer. The creators claim the tool will only serve to boost productivity and free developers from doing manual tasks and help them focus on more interesting work. It might also be possible it lowers the barriers for beginners to enter the software industry.

I recently started an AI-focused educational newsletter, that already has over 90,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Interpreting and understanding the behavior of deep neural networks remains one of the main challenges in the deep learning ecosystem. As humans, we regularly try to understand new subjects by comparing them to other knowledge areas we are familiar with.

In a country such as India that has a low doctor-patient ratio, Artificial Intelligence (AI) can enable greater access to expert care from anywhere, with telehealth and robotics applied across inpatient and outpatient environments. Experts says AI will bolster the role of healthcare by assisting in screening, diagnosis, and treatment of diseases thereby improving quality of life and reducing the cost burden for patients. "India has a tremendous opportunity to lead human-centric applications and democratise AI for the world backed by high skilled talent, technology, vast data availability, and the potential for population-scale AI adoption," says Prakash Mallya, vice-president and managing director of Sales, Marketing and Communications Group, Intel India. Intel has been focusing its efforts towards accelerating AI innovation to deliver transformative healthcare solutions and democratise healthcare access and delivery in India. The company's portfolio of compute, memory, storage, and networking technologies powers some of the most exciting healthcare and life sciences applications. The cloud-based AI solution Netra.AI is the latest example of the impact and innovation that can be made possible with Intel technology.

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The fraud usually happens when someone obtains your credit or debit card numbers through unprotected websites or through an identity theft scheme in order to get money or property fraudulently. Because of the frequency with which it occurs and the potential harm it may bring to both individuals and financial institutions, it is critical to take preventative steps as well as recognize when a transaction is fraudulent. Data-set can be downloaded from the above link. As the data-set is highly imbalanced, there is a need for us to balance it, in order to get classes to close proximity.

In 2021, the average annual salary for a'machine learning engineer' in India is INR 8 lakh ($10,757), as per Glassdoor. But according to PayScale, the median salary of an ML engineer stands at INR 6.8 lakh per year($9,382). Artificial intelligence and machine learning jobs are projected to grow 40% over the next six years. LinkedIn has 1,982 job listings for candidates with TensorFlow expertise, 1,176 open positions for candidates with PyTorch knowledge. At the time of writing, LinkedIn had 15,542 jobs in India and 171,549 open positions worldwide with machine learning as a required skill.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Deep neural networks will move past their shortcomings without help from symbolic artificial intelligence, three pioneers of deep learning argue in a paper published in the July issue of the Communications of the ACM journal. In their paper, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, recipients of the 2018 Turing Award, explain the current challenges of deep learning and how it differs from learning in humans and animals. They also explore recent advances in the field that might provide blueprints for the future directions for research in deep learning.

We study gradient-based regularization methods for neural networks. We mainly focus on two regularization methods: the total variation and the Tikhonov regularization. Applying these methods is equivalent to using neural networks to solve some partial differential equations, mostly in high dimensions in practical applications. In this work, we introduce a general framework to analyze the generalization error of regularized networks. The error estimate relies on two assumptions on the approximation error and the quadrature error. Moreover, we conduct some experiments on the image classification tasks to show that gradient-based methods can significantly improve the generalization ability and adversarial robustness of neural networks. A graphical extension of the gradient-based methods are also considered in the experiments.

Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games. Search-based methods, however, can hardly model patterns of danmakus -- the bullet hell shooting entity -- explicitly and the resulting levels often look non-realistic. In this paper, we present a novel bullet hell game platform named Keiki, which allows the representation of danmakus as a parametric sequence which, in turn, can model the sequential behaviours of danmakus. We employ three types of generative adversarial networks (GANs) and test Keiki across three metrics designed to quantify the quality of the generated danmakus. The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games.

Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, complex embodied communication in multimodal settings within constantly evolving social worlds. We explain how concepts from cognitive sciences could help AI to draw a roadmap towards human-like intelligence, with a focus on its social dimensions. As a first step, we propose to expand current research to a broader set of core social skills. To do this, we present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents using multiple grid-world environments featuring other (scripted) social agents. We then study the limits of a recent SOTA DRL approach when tested on SocialAI and discuss important next steps towards proficient social agents. Videos and code are available at https://sites.google.com/view/socialai.