"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
OpenAI has developed a neural network that can play Minecraft like humans. The Artificial Intelligence (AI) model was trained over 70,000 hours of miscellaneous in-game footage, along with a small database of videos in which specific in-game tasks were performed. Keyboard and mouse inputs are also recorded. OpenAI fine-tuned the AI, and now, it is skillful as a human-it can swim, hunt for animals, and eat. The AI can also do the pillar jump, where a player places a block of material below themselves in mid-air to gain more elevation.
Sinequa said its neural search function can answer natural language questions, thanks to four deep learning models it developed with Microsoft Azure and Nvidia teams. Enterprise search company Sinequa is adding a neural search option to its platform with the aim of giving improved accuracy and relevance to customers. Sinequa said the new AI function is the first commercially available system to use four deep learning language models. Combined with the platform's natural language processing and semantic search abilities, Sinequa said this will lead to improved question-answering and search relevance. The Sinequa Search Cloud platform is designed to help employees find relevant information and insights from all enterprise sources in any language in the context of their work.
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. It's simple: In financial services, customer data offers the most relevant services and advice. But, oftentimes, people use different financial institutions based on their needs – their mortgage with one; their credit card with another; their investments, savings and checking accounts with yet another. And in the financial industry more so than others, institutions are notoriously siloed. Largely because the industry is so competitive and highly regulated, there hasn't been much incentive for institutions to share data, collaborate or cooperate in an ecosystem. Customer data is deterministic (that is, relying on first-person sources), so with customers "living across multiple parties," financial institutions aren't able to form a precise picture of their needs, said Chintan Mehta, CIO and head of digital technology and innovation at Wells Fargo.
Imagine a deepfake video of House Speaker Nancy Pelosi, in which her speech is intentionally slurred and the words she uses are changed to deliver a message that's offensive to large numbers of voters. Now imagine that the technology used to create the video is so sophisticated that it appears completely real, rendering the manipulation undetectable, unlike clumsy deepfakes of Pelosi that circulated--and were quickly debunked--in 2020 and 2021. What would be the impact of such a video on closely contested House races in a midterm election? That's the dilemma Adobe, maker of the world's most popular tools for photo and video editing, faces as it undergoes a top-to-bottom review and redesign of its product mix using artificial intelligence and deep learning techniques. That includes upgrades to the company's signature Photoshop software and Premiere Pro video-editing tool.
We explained the 5 Ws of artificial intelligence in developing countries. In recent years, artificial intelligence hasn't had a very favorable reputation overall. It is considered a threat to human employment opportunities even though we use artificial intelligence in everyday life. Is artificial intelligence better than human intelligence? The answer to this question will differ from person to person, but there is something that cannot be denied.
Embedded machine learning (ML) systems have now become the dominant platform for deploying ML serving tasks and are projected to become of equal importance for training ML models. With this comes the challenge of overall efficient deployment, in particular low power and high throughput implementations, under stringent memory constraints. In this context, non-volatile memory (NVM) technologies such as STT-MRAM and SOT-MRAM have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While prior work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM, a comprehensive framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technology-specific circuit-level models and the actual memory behavior of various DL workloads. DeepNVM relies on iso-capacity and iso-area performance and energy models for last-level caches implemented using conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies.
The past month has seen a frenzy of articles, interviews, and other types of media coverage about Blake Lemoine, a Google engineer who told The Washington Post that LaMDA, a large language model created for conversations with users, is "sentient." After reading a dozen different takes on the topic, I have to say that the media has become (a bit) disillusioned with the hype surrounding current AI technology. A lot of the articles discussed why deep neural networks are not "sentient" or "conscious." This is an improvement in comparison to a few years ago, when news outlets were creating sensational stories about AI systems inventing their own language, taking over every job, and accelerating toward artificial general intelligence. But the fact that we're discussing sentience and consciousness again underlines an important point: We are at a point where our AI systems--namely large language models--are becoming increasingly convincing while still suffering from fundamental flaws that have been pointed out by scientists on different occasions.
Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This is a massive 4-in-1 course covering: 1) Vector models and text preprocessing methods 2) Probability models and Markov models 3) Machine learning methods 4) Deep learning and neural network methods In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Document retrieval / search engine Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.
A while back, I was on Twitter and I saw the following exceptional take on Twitter. This is a very popular idea online, and I had meant to write about this sooner. But thanks to all the insanity happening in the ML research domain, I got side-tracked. However, now that I have the time I can finally cover this in-depth. In this post, I will cover "Why you absolutely need Math for Machine Learning."