Tatum, Texas might not seem like the most obvious place for a revolution in artificial intelligence (AI), but in October of 2020, that's exactly what happened. That was when Wayne Brown, the operations manager at the Vistra-owned Martin Lake Power Plant, built and deployed a heat rate optimizer (HRO). Vistra Corp. is the largest competitive power producer in the United States and operates power plants in 12 states with a capacity of more than 39,000 megawatts of electricity--enough to power nearly 20 million homes. Vistra has committed to reducing emissions by 60 percent by 2030 (against a 2010 baseline) and achieving net-zero emissions by 2050. To achieve its goals, the business is increasing efficiency in all its power plants and transforming its generation fleet by retiring coal plants and investing in solar- and battery-energy storage, which includes the world's largest grid-scale battery energy-storage facility.
DeepMind's Gato may or may not be a major breakthrough for AI DeepMind has released what it calls a "generalist" AI called Gato, which can play Atari games, accurately caption images, chat naturally with a human and stack coloured blocks with a robot arm, among 600 other tasks. But is Gato truly intelligent – having artificial general intelligence – or is it just an AI model with a few extra tricks up its sleeve? What is artificial general intelligence (AGI)? Outside science fiction, AI is limited to niche tasks. It has seen plenty of success recently in solving a huge range of problems, from writing software to protein folding and even creating beer recipes, but individual AI models have limited, specific abilities.
DeepMind, a British company owned by Google, may be on the verge of achieving human-level artificial intelligence (AI). Nando de Freitas, a research scientist at DeepMind and machine learning professor at Oxford University, has said'the game is over' in regards to solving the hardest challenges in the race to achieve artificial general intelligence (AGI). AGI refers to a machine or program that has the ability to understand or learn any intellectual task that a human being can, and do so without training. According to De Freitas, the quest for scientists is now scaling up AI programs, such as with more data and computing power, to create an AGI. Earlier this week, DeepMind unveiled a new AI'agent' called Gato that can complete 604 different tasks'across a wide range of environments'. Gato uses a single neural network – a computing system with interconnected nodes that works like nerve cells in the human brain.
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.
The ultimate achievement to some in the AI industry is creating a system with artificial general intelligence (AGI), or the ability to understand and learn any task that a human can. Long relegated to the domain of science fiction, it's been suggested that AGI would bring about systems with the ability to reason, plan, learn, represent knowledge, and communicate in natural language. Not every expert is convinced that AGI is a realistic goal -- or even possible. Gato is what DeepMind describes as a "general-purpose" system, a system that can be taught to perform many different types of tasks. Researchers at DeepMind trained Gato to complete 604, to be exact, including captioning images, engaging in dialogue, stacking blocks with a real robot arm, and playing Atari games. Jack Hessel, a research scientist at the Allen Institute for AI, points out that a single AI system that can solve many tasks isn't new.
Is AI just a black box that we started trusting enough to drive cars, detect diseases, identify suspects just because of the hype? You may have heard of the Netflix documentary, Coded Bias (you can watch the film here). The film criticizes deep learning algorithms for their inherent biases; specifically their failure to detect dark-skinned and female faces. The film suggests that the solution to the problem is in government. To "push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us all."
In Her, the 2013 science fiction movie portraying the love between a human and an AI, the behaviors of AI can be explainable. As a result, she can help humans make decisions and even generate deep communication with humans. Unfortunately, these overly idealize scenarios can only exist in science fiction works for now. The explainable AI in practice are not up to standard neither in the aspect of technology nor in the aspect of experiences. Why is AI required to be explainable? Or a more fundamental question would be: why is AI considered as a black box?
The Rubik's Cube is a famous 3-D puzzle toy. A regular Rubik's Cube has six faces, each of which has nine coloured stickers, and the puzzle is solved when each face has a united colour. If we count one quarter (90) turn as one move and two quarter turns (a "face" turn) as two moves, the best algorithms human-invented can solve any instance of the cube in 26 moves. My target is to let the computer learn how to solve the Rubik's Cube without feeding it any human knowledge like the symmetry of the cube. The most challenging part is the Rubik's Cube has 43,252,003,274,489,856,000 possible permutations.
The negative applications of deepfakes can be controlled through blockchain and other deep learning-based image forgery detection tools. However, there is more to deepfakes than just negative applications. Ever since the emergence of deepfakes, they have been normally associated with pranks or cybercrimes. Accordingly, there are several pieces that discuss the ways in which deepfake-related problems can be resolved through blockchain or deep learning-based image forgery detection. The concept of deepfakes, also known as synthetic media, is one of the more irreverent applications of AI and computer vision.
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