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It Happened One Frame: incredibly accurate video content search with OpenAI CLIP

#artificialintelligence

I love movies, so as a fun exercise for my fast.ai It's named "It Happened One Frame", in tribute to the classic 1934 romantic comedy "It Happened One Night". To use this app, all you need is the link to a Youtube video. For example, you could search "Macaulay Culkin screams with hands on his cheeks" in a Home Alone movie clip and get the screenshots that capture the most iconic scene in this classic. This particular image is so popular that you can easily get it from a google search.


AI Predictions For 2022 And Beyond - Liwaiwai

#artificialintelligence

We have all been down the same train of thought regarding artificial intelligence, thanks to sci-fi films peppered throughout the history of Hollywood: Artificial intelligence is too dangerous. However, as time has proven over and over again, humankind is unable to duplicate the same kind of AI that we see in the movies..yet. Any attempt at creating artificial intelligence is garnered towards machine learning and semantic similarity. We're still a long way from sentient A.I, but here's what's going on in the industry and what we can expect moving forward. The prestigious business magazine recognizes the fact that we already utilize A.I. in our day to day, and is playing an important role in automation.


AI Predictions for 2022 and Beyond

#artificialintelligence

I find myself watching crypto charts more than movies these days. We have all been down the same train of thought regarding artificial intelligence, thanks to sci-fi films peppered throughout the history of Hollywood: Artificial intelligence is too dangerous. However, as time has proven over and over again, humankind is unable to duplicate the same kind of AI that we see in the movies..yet. Any attempt at creating artificial intelligence is garnered towards machine learning and semantic similarity. We're still a long way from sentient A.I, but here's what's going on in the industry and what we can expect moving forward.


The Development of Artificial Intelligence in Everyday Life

#artificialintelligence

As time goes by, it is more frequent that the use of artificial intelligence supports the activities we perform on a daily basis. Have you done a Google search recently, used Siri on your cell phone in the last few days, watched a movie on Netflix, played online games, listened to music on Spotify, or compared something on Amazon lately? If you've done any of these things, you've certainly come into contact with some artificial intelligence development. Over time, it is more frequent that artificial intelligence supports the activities we do daily. Every day, companies that have access to our data know us better and provide us with a better service.


Boosting Search Engines with Interactive Agents

arXiv.org Artificial Intelligence

Can machines learn to use a search engine as an interactive tool for finding information? That would have far reaching consequences for making the world's knowledge more accessible. This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based generative language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that can learn interactive search strategies completely from scratch. In both cases, we obtain significant improvements over one-shot search with a strong information retrieval baseline. Finally, we provide an in-depth analysis of the learned search policies.


Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates

arXiv.org Machine Learning

We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of items, users), and ii) the best known privacy/utility trade-off both theoretically, as well as on benchmark data sets. In particular, we provide the first global convergence analysis of ALS with noise introduced to ensure DP, and show that, in comparison to the best known alternative (the Private Frank-Wolfe algorithm by Jain et al. (2018)), our error bounds scale significantly better with respect to the number of items and users, which is critical in practical problems. Extensive validation on standard benchmarks demonstrate that the algorithm, in combination with carefully designed sampling procedures, is significantly more accurate than existing techniques, thus promising to be the first practical DP embedding model.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


8 Examples of Artificial Intelligence in our Everyday Lives

#artificialintelligence

The applications of artificial intelligence have grown over the past decade. Here are examples of artificial intelligence that we use in our everyday lives. The words artificial intelligence may seem like a far-off concept that has nothing to do with us. But the truth is that we encounter several examples of artificial intelligence in our daily lives. From Netflix's movie recommendation to Amazon's Alexa, we now rely on various AI models without knowing it.


8 Examples of Artificial Intelligence in our Everyday Lives

#artificialintelligence

The applications of artificial intelligence have grown exponentially over the past decade. Here are some examples of artificial intelligence at work today. The words artificial intelligence may seem like a far-off concept that has nothing to do with us. But the truth is that we encounter several examples of artificial intelligence in our daily lives. From Netflix's movie recommendation to Amazon's Alexa, we now rely on various AI models without knowing it.


One Network to Fit All Hardware: New MIT AutoML Method Trains 14X Faster Than SOTA NAS

#artificialintelligence

AI is now integrated into countless scenarios, from tiny drones to huge cloud platforms. Every hardware platform is ideally paired with a tailored AI model that perfectly meets requirements in terms of performance, efficiency, size, latency, etc. However even a single model architecture type needs tweaking when applied to different hardware, and this requires researchers spend time and money training them independently. Popular solutions today include either designing models specialized for mobile devices or pruning a large network by reducing redundant units, aka model compression. A group of MIT researchers (Han Cai, Chuang Gan and Song Han) have introduced a "Once for All" (OFA) network that achieves the same or better level accuracy as state-of-the-art AutoML methods on ImageNet, with a significant speedup in training time. A major innovation of the OFA network is that researchers don't need to design and train a model for each scenario, rather they can directly search for an optimal subnetwork using the OFA network.