Collaborating Authors

Representation & Reasoning

Regulators should encourage adoption of fair-lending algorithms


In 1869, the English judge Baron Bramwell rejected the idea that "because the world gets wiser as it gets older, therefore it was foolish before." Financial regulators should adopt this same reasoning when reviewing financial institutions' efforts to make their lending practices fairer using advanced technology like artificial intelligence and machine learning. If regulators don't, they risk holding back progress by incentivizing financial institutions to stick with the status quo rather than actively look for ways to make lending more inclusive. The simple, but powerful, concept articulated by Bramwell underpins a central public policy pillar: You can't use evidence that someone improved something against them to prove wrongdoing. In law this is called the doctrine of "subsequent remedial measures." It incentivizes people to continually improve products, experiences and outcomes without fear that their efforts will be used against them.

Yann LeCun's vision for creating autonomous machines


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. In the midst of the heated debate about AI sentience, conscious machines and artificial general intelligence, Yann LeCun, Chief AI Scientist at Meta, published a blueprint for creating "autonomous machine intelligence." LeCun has compiled his ideas in a paper that draws inspiration from progress in machine learning, robotics, neuroscience and cognitive science. He lays out a roadmap for creating AI that can model and understand the world, reason and plan to do tasks on different timescales. While the paper is not a scholarly document, it provides a very interesting framework for thinking about the different pieces needed to replicate animal and human intelligence. It also shows how the mindset of LeCun, an award-winning pioneer of deep learning, has changed and why he thinks current approaches to AI will not get us to human-level AI.

Best 15 real-life examples of machine learning - Dataconomy


Numerous examples of machine learning show that machine learning (ML) can be extremely useful in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. In all of these areas and more, ML offers viable solutions, and it is destined to be a cornerstone of our post-apocalyptic civilization. The history of machine learning shows that a good grasp of the machine learning lifecycle increase machine learning benefits for businesses significantly. There are many uncommon machine learning examples that prove this, and you will find the best ones in this article. Machine learning uses statistical methods to increase a computer's intelligence, assisting in the automatic utilization of all business data. Due to growing reliance on machine learning technologies, humans' lifestyles have undergone a significant transformation. We use Google Assistant, which uses ML principles, as an example.

OnePlus 7 and 7T obtain the OxygenOS 12 (Android 12) beta, lastly - Channel969


On the OnePlus neighborhood boards, OxygenOS Operations Supervisor Abdul B. introduced on June 30 that the OxygenOS 12 beta is out there for the OnePlus 7, OnePlus 7T, their Professional variations, and the OnePlus Nord CE. The OnePlus 9RT lastly acquired the OxygenOS 12 beta primarily based on Android 12 final week. With this new batch of telephones, OnePlus has formally introduced the beta to almost each Android 12-eligible telephone. Sadly, it is solely the Indian software program model that is presently obtainable for obtain, not the World model. Abdul B. notes that international customers "could introduce unexpected bugs or potential points" by downloading it and advises you "anticipate different regional variations to roll out."

Summer 2022 - Researcher positions in artificial intelligence and machine learning -- FCAI


We develop reinforcement learning techniques to enable interaction across multiple agents including AIs and humans, with potential applications from AI-assisted design to autonomous driving. Methodological contexts of the research include deep reinforcement learning, inverse reinforcement learning, hierarchical reinforcement learning as well as multi-agent and multi-objective reinforcement learning. FCAI is working on a new paradigm of AI-assisted design that aims to cooperate with designers by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We use generative user models to reason about designers' goals, reasoning, and capabilities. In this call, FCAI is looking for a postdoctoral scholar or research fellow to join our effort to develop AI-assisted design. Suitable backgrounds include deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modelling, and user modelling. Computational rationality is an emerging integrative theory of intelligence in humans and machines (1) with applications in human-computer interaction, cooperative AI, and robotics. The theory assumes that observable human behavior is generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself (2).

Why are AI and Machine Learning Important? -


AI refers to Artificial intelligence is known as the ability of a digital computer or robot to perform tasks associated with intelligent begins. It is the simulation of human intelligence processes by machines like computers, robots, etc. In this smart and advanced process, intelligence is demonstrated by machines. Here machines can mimic human intelligence to perform tasks and can improve themselves based on the information they collect. We can see Al's98 techniques in new innovations like chatbots ( used to understand customers' problems), intelligent assistants ( parse critical information to improve scheduling), Recommendation engines ( provide recommendations for TV shows), etc. AI can be related to processes and capability for superpowered thinking and data analysis.

Prime members can snag a refurbished Echo at a very good discount


SAVE $37: Want to turn your home into a smart home? As of July 1, Amazon Prime members can save $37 on a certified refurbished fourth-generation Echo(opens in a new tab). Budget and eco-conscious shoppers, this certified refurbished Echo deal(opens in a new tab) is worth checking out ahead of Prime Day. This recent price drop on the certified refurbished Echo brings it to the lowest price we've seen to date. It also beats the lowest historical price for a new Echo ($59.99), which we haven't seen since around Black Friday last year. By opting for refurbished, you're saving money and taking otherwise functional Echos out of the waste stream -- it's a total win-win.

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning


We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. This popular game has an enormous game tree on the order of $10^{535}$ nodes, i.e., $10^{175}$ times larger than that of Go. It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes). Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. Episodes are long, with often hundreds of moves before a player wins, and situations in Stratego can not easily be broken down into manageably-sized sub-problems as in poker. For these reasons, Stratego has been a grand challenge for the field of AI for decades, and existing AI methods barely reach an amateur level of play. DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play. The Regularised Nash Dynamics (R-NaD) algorithm, a key component of DeepNash, converges to an approximate Nash equilibrium, instead of 'cycling' around it, by directly modifying the underlying multi-agent learning dynamics. DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly (2022) and all-time top-3 rank on the Gravon games platform, competing with human expert players.

Best dating sites and apps for people over 40 -- and which ones to avoid


Dating when you're 40 or older can be intimidating -- unlike when you're in your 20s or 30s, you can't assume everyone your age is single and looking. If you've found yourself "on the market" again, it's important to remember that half of U.S. marriages do end in divorce, so the dating pool isn't as small as you might think. Meeting people organically out in public still happens, but sometimes it's easier and less intimidating to meet people where they are. There's a comfort in knowing that the people you find on dating apps are single (hopefully) and looking for a romantic relationship, so at least you're both on the same page. The first step is just acknowledging that you're ready.