Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind's work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential for real world applications of RL should also come with a healthy dose of caution – for example RL policies are well known to be vulnerable to exploitation, and methods for safe and robust policy development are an active area of research. At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. The focus of these research efforts to date has been to account for shortcomings of datasets or supervised learning practices that can harm individuals.

According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.

Networking plays a critical role in one's professional life, particularly for people who depend on experts' opinions and hold curiosity about lived experiences.LinkedIn lets users connect to people of their choice, exchange ideas, or collaborate on projects. However, communicating directly with them comes at a price. Email messages for LinkedIn where users can message users directly is a premium feature. LinkedIn groups play a vital role in bringing together the tech enthusiasts to collaborate and brainstorm ideas; and it works particularly well in areas like artificial intelligence and machine learning, where you can get inputs from people who are experienced enough to instill a sense of direction and provide significant insights. It is a collaborative group of AI researchers who work on next-generation machine intelligence.

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The Alphabet subsidiary DeepMind has done it again, and this time, they are testing the boundaries of AI in software development sectors. DeepMind's AlphaCode was tested against human performance on coding challenges and achieved rank among the top 54% of human coders on Codeforces. This is a remarkable achievement as it is one of its kind. There are other code generation machine learning models, such as OpenAI Codex, but none of them tried to compete with human programmers. A coding challenge is like solving puzzles. To solve these challenges, an individual must have an understanding of logic, math, and programming skills.

Google I/O 2022, the most awaited developers' conference of the year, is around the corner. With more than 200 speakers, the summit will cover a broad spectrum of topics and will have a slew of announcements on the latest innovations in AI and ML. The I/O adventure also makes a comeback this year: Users can explore the platform to see product demos, chat with Googlers, earn Google Developer profile badges and virtual swag, engage with the developer community, create an avatar, and look for easter eggs. Seek out your next Adventure at Google I/O 2022! The conference is scheduled to start at 10:30 pm IST on May 11, 2022, and will kick off with Alphabet CEO Sundar Pichai's keynote speech.

One of the coolest parts of building machine learning models is sharing the models we built with others. No matter how many models you've built, if they stay offline, only very few people will be able to see what you've accomplished. This is why we should deploy our models, so anyone can play with them through a nice UI. Flask is a Python framework that lets us develop web applications easily. After following this guide, you'll be able to play with a simple machine learning model in your browser as shown in the gif below.

Thirteen university students from across Canada are in Ottawa to put their artificial intelligence skills to the test. It's called the Amazon Web Services DeepRacer League, where small 1/18th scale cars are being trained to complete a racetrack as fast as possible, by themselves. "It has major components in order to do the autonomous driving," says Amanda Foo, DeepRacer Senior Technical Program Manager. They are driven by what is called reinforcement learning. "It's just like training a dog," Carleton University mechanical engineering student Masoud Karimi says.

What is a smart city? The rise of the Internet of Things (IoT) has driven the development of smart cities. Cybersecurity appears a lot in the news and is generally a bad thing. TOP 10 DEEP LEARNING PROJECT Most research fields require large amounts of funding and well-equipped laboratories. But to work with DL at a... What is the average program developers salary? Today, we are going to learn about the key implications and lifestyles of program develop...

Chip shortages are forcing fabs and OSATs to maximize capacity and assess how much benefit AI and machine learning can provide. This is particularly important in light of the growth projections by market analysts. The chip manufacturing industry is expected to double in size over the next five years, and collective improvements in factories, AI databases, and tools will be essential for doubling down on productivity. "We're not going to fail on this digital transformation, because there's no option," said John Behnke, general manager in charge of smart manufacturing at Inficon. "All the fabs are collectively going to make 20% to 40% more product, but they can't get a new tool right now for 18 to 36 months. To leverage all this potential, we're going to overcome the historical human fear of change."