"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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This is the second edition of my weekly update on deep learning. Every Thursday, I'll release a new batch of research papers, blog posts, Github repos, etc. that I liked over the past week. Links are provided for each featured project, so you can dive in and learn about whatever catches your eye. If you missed last week's edition, you can find it here. All thoughts and opinions are my own.
My goal throughout will be to understand not just how something works but why it works that way. If you haven't read the earlier articles, particularly the second and third ones, it would be a good idea to read them first, as this article builds on many of the concepts that we discussed there. Q-Learning is the most interesting of the Lookup-Table-based approaches which we discussed previously because it is what Deep Q Learning is based on. The Q-learning algorithm uses a Q-table of State-Action Values (also called Q-values). This Q-table has a row for each state and a column for each action. Each cell contains the estimated Q-value for the corresponding state-action pair. We start by initializing all the Q-values to zero. As the agent interacts with the environment and gets feedback, the algorithm iteratively improves these Q-values until they converge to the Optimal Q-values.
A new AI-powered video-editing platform is preparing for launch, designed to help businesses, marketers, and creators automatically transform landscape-shot videos into a vertical format suitable for TikTok, Instagram, Snapchat, and all the rest. Founded out of London in 2019, Kamua wants to be aligned with tools such as Figma, a software design and prototyping tool for product managers who lack certain technical skills. For Kamua, the goal is democratizing the creative and technical processes in video editing. "Kamua makes it possible for non-editors to directly control how their videos look in any format, on any screen, in multiple durations and sizes, without the steep and long learning curves, hardware expense, and legacy workflows associated with editing software suites," Kamua CEO and cofounder Paul Robert Cary told VentureBeat. Kamua, which was available as an alpha release since last year before launching in invite-only beta back in September, is now preparing for a more extensive roll-out on December 1, when a limited free version will be made available for anyone without any formal application process.
When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.
In the world of Machine Learning, I find the K-Nearest Neighbors (KNN) classifier makes the most intuitive sense and easily accessible to beginners even without introducing any math notations. To decide the label of an observation, we look at its neighbors and assign the neighbors' label to the observation of interest. Certainly, looking at one neighbor may create bias and inaccuracy, and the KNN method has a set of rules and procedures to determine the best number of neighbors, e.g., examining k 1 neighbors and adopt majority rule to decide the category. "To decide the label for new observations, we look at the closest neighbors." To choose the nearest neighbors, we have to define what distance is.
We've been seeing the headlines for years: "Researchers find flaws in the algorithms used…" for nearly every use case for AI, including finance, health care, education, policing, or object identification. Most conclude that if the algorithm had only used the right data, was well vetted, or was trained to minimize drift over time, then the bias never would have happened. There are several practical strategies that you can adopt to instrument, monitor, and mitigate bias through a disparate impact measure. For models that are used in production today, you can start by instrumenting and baselining the impact live. For analysis or models used in one-time or periodic decision making, you'll benefit from all strategies except for live impact monitoring.
In an effort to be the first organization to bring true artificial intelligence (AI) to the 401k market, The 401(k) Plan Company recently announced a minority investment in Unanimous AI to help elevate human decision-making in the workplace for HR partners, CFOs and plan participants. San Francisco-based Unanimous AI builds technologies that amplify human intelligence using technologies modeled on the biological principle of Swarm Intelligence. Unanimous AI in late October announced it has been awarded three new U.S. Patents covering its unique AI technology aimed at amplifying the intelligence of human groups. Swarm AI technology from Unanimous is a combination of real-time human input and AI algorithms, which the company says enables networked groups of people to think together as super-intelligent systems. Under terms of the deal, The 401(k) Plan Company will make Unanimous AI's capabilities available to employers seeking to evolve through the remote workforce considerations, empowering teams to make significantly better decisions. "AI has the power to replace humans, or to amplify their best work.