If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Without much prior experience, kids can recognize other people's intentions and come up with plans to help them achieve their goals, even in novel scenarios. That's why researchers at MIT, Nvidia, and ETH Zurich developed Watch-And-Help (WAH), a challenge in which embodied AI agents need to understand goals by watching a demonstration of a human performing a task and coordinating with the human to solve the task as quickly as possible. The concept of embodied AI draws on embodied cognition, the theory that many features of psychology -- human or otherwise -- are shaped by aspects of the entire body of an organism. By applying this logic to AI, researchers hope to improve the performance of AI systems like chatbots, robots, autonomous vehicles, and even smart speakers that interact with their environments, people, and other AI. A truly embodied robot could check to see whether a door is locked, for instance, or retrieve a smartphone that's ringing in an upstairs bedroom.
A pair of statisticians at the University of Waterloo has proposed a math process idea that might allow for teaching AI systems without the need for a large dataset. Ilia Sucholutsky and Matthias Schonlau have written a paper describing their idea and published it on the arXiv preprint server. Artificial intelligence (AI) applications have been the subject of much research lately, with the development of deep learning networks, researchers in a wide range of fields began finding uses for it, including creating deepfake videos, board game applications and medical diagnostics. Deep learning networks require large datasets in order to detect patterns revealing how to perform a given task, such as picking a certain face out of a crowd. In this new effort, the researchers wondered if there might be a way to reduce the size of the dataset.
Recent years have witnessed a swell in the adoption of artificial intelligence solutions, revolutionising industries, and helping businesses boost growth. The rising volume and complexity of business data are set to continue driving AI adoption in the following years, causing a surge in global AI spending. According to data presented by BuyShares, global artificial intelligence spending is expected to surge by 120% and hit $110bn by 2024. Businesses across the world use AI technology to be innovative and scalable. Using automation, deep learning, and natural language processing can improve their decision-making, efficiency, speed, and help predict trends.
Artificial Intelligence and machine learning have been hot topics in 2020 as AI and ML technologies increasingly find their way into everything from advanced quantum computing systems and leading-edge medical diagnostic systems to consumer electronics and "smart" personal assistants. Revenue generated by AI hardware, software and services is expected to reach $156.5 billion worldwide this year, according to market researcher IDC, up 12.3 percent from 2019. But it can be easy to lose sight of the forest for the trees when it comes to trends in the development and use of AI and ML technologies. As we approach the end of a turbulent 2020, here's a big-picture look at five key AI and machine learning trends– not just in the types of applications they are finding their way into, but also in how they are being developed and the ways they are being used. Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated – such as legacy business processes – should be automated.
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Ensuring fairness and safety in artificial intelligence(AI) applications is considered by many the biggest challenge in the space. As AI systems match or surpass human intelligence in many areas, it is essential that we establish a guideline to align this new form of intelligence with human values.
According to EuroNews, there is a fascinating project being undertaken right now, which is that scientists are using satellites and AI technology to map every single tree on Earth. Scientists have mapped 1.8 billion individual tree canopies across millions of kilometres of the Sahel and Sahara regions of West Africa. It is the first time ever that trees have been mapped in detail over such a large area. So how was it possible? They employed neural networks which are able to recognise objects, like trees, based on their shapes and colours. To train it, the AI system was shown satellite images where trees had been manually traced.
Microsoft and non-profit research organization MITRE have joined forces to accelerate the development of cyber-security's next chapter: to protect applications that are based on machine learning and are at risk of new adversarial threats. The two organizations, in collaboration with academic institutions and other big tech players such as IBM and Nvidia, have released a new open-source tool called the Adversarial Machine Learning Threat Matrix. The framework is designed to organize and catalogue known techniques for attacks against machine learning systems, to inform security analysts and provide them with strategies to detect, respond and remediate against threats. What is AI? Everything you need to know about Artificial Intelligence The matrix classifies attacks based on criteria related to various aspects of the threat, such as execution and exfiltration, but also initial access and impact. To curate the framework, Microsoft and MITRE's teams analyzed real-world attacks carried out on existing applications, which they vetted to be effective against AI systems.
Machine learning is taking medical diagnosis by storm. From eye disease, breast and other cancers, to more amorphous neurological disorders, AI is routinely matching physician performance, if not beating them outright. Yet how much can we take those results at face value? When it comes to life and death decisions, when can we put our full trust in enigmatic algorithms--"black boxes" that even their creators cannot fully explain or understand? The problem gets more complex as medical AI crosses multiple disciplines and developers, including both academic and industry powerhouses such as Google, Amazon, or Apple, with disparate incentives.
Kevin Gray: AI has become part of our daily lives, hasn't it! Dr. Anna Farzindar: I was working on my laptop when my college daughter said "Mom please don't do anything wrong with AI!" Then two days later during our family dinner, my younger freshman high school daughter told a story about a video on social media showing a small home care robot that tricked the owner and lied. She asked me "Mom, aren't you afraid of robots?" These short conversations made me think about how the new generation is a big consumer of technology but, at the same time, they are concerned and worried about the future AI. KG: Getting back to basics, what is AI? AF: From talking to your virtual assistance on smartphone (like SIRI), watching a recommended movie on Netflix, searching on Google, following the suggested Instagram posts, using the sophisticated methods of an auto trading stock market, applying the decision making systems for your loan approval, or (soon) sitting in a self-driving car, AI algorithms are so embedded in our daily life that is hard to imagine living a single day without them!
In case anyone missed it: attention on AI's application to healthcare is apparently at'peak hype'. With the volume of healthcare data doubling every 2 to 5 years, it is no surprise that many are using AI to make sense of such vast amounts of data, and development of medical AI technologies is progressing rapidly. At the same time, the COVID-19 pandemic has exposed vulnerabilities in healthcare systems around the world, highlighting the need for technological interventions in healthcare. In line with these trends, the healthcare AI market is expected to grow from US$2 billion in 2018 to US$36 billion by 2025. The breadth of AI's application in healthcare is impressive, ranging from diagnostic chat bots to AI robot-assisted surgery.