deep learning


Deep Learning Chatbots: Everything You Need to Know

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When you're creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods. With the first method, the customer service team receives suggestions from AI to improve customer service methods. The second method involves a deep learning chatbot, which handles all of the conversations itself and removes the need for a customer service team. Such is the power of chatbots that the number of chatbots on Facebook Messenger increased from 100K to 300K within just 1 year.



How AI Accelerators Are Changing The Face Of Edge Computing

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AI has become the key driver for the adoption of edge computing. Originally, the edge computing layer was meant to deliver local compute, storage, and processing capabilities to IoT deployments. Sensitive data that cannot be sent to the cloud for processing and analysis is handled by the edge. It also reduces the latency involved in the roundtrip to the cloud. Most of the business logic that runs in the cloud is moving to the edge to provide low-latency, faster response time.


Creating a neural network from scratch in JavaScript -- Part 1.2

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In this part, we're going to create a simple example of how we can use the neuron.js Throughout this series, we will try to describe how to use this code to build neural networks, AI bots, and potentially our own deep learning framework. There are many great resources on the fundamentals of neural networks, how they work, why they work, but there are few articles on how to actually build neural networks from a software development perspective. With this very "technical" series, I hope to address this; for a quick introduction, checkout this simple introduction to neural networks, Eric Elliot's article on neurons, or 3Blue1Brown's video series on neural networks. In this example we will be teaching a neural network created with the neuron.js


The Long Game of Research

Communications of the ACM

The Institute for the Future (IFTF) in Palo Alto, CA, is a U.S.-based think tank. It was established in 1968 as a spin-off from the RAND Corporation to help organizations plan for the long-term future. Roy Amara, who passed away in 2007, was IFTF's president from 1971 until 1990. Amara is best known for coining Amara's Law on the effect of technology: "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run." This law is best illustrated by the Gartner Hype Cycle,a characterized by the "peak of inflated expectations," followed by the "trough of disillusionment," then the "slope of enlightenment," and, finally, the "plateau of productivity."


As Search Engines Increasingly Turn To AI They Are Harming Search

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For more than half a century our digital search engines have relied upon the humble keyword. Yet over the past few years, search engines of all kinds have increasingly turned to deep learning-powered categorization and recommendation algorithms to augment and slowly replace the traditional keyword search. Behavioral and interest-based personalization has further eroded the impact of keyword searches, meaning that if ten people all search for the same thing, they may all get different results. As search engines depreciate traditional raw "search" in favor of AI-assisted navigation, the concept of informational access is being harmed and our digital world is being redefined by the limitations of today's AI. At first glance, the evolution of search from simple TF-IDF keyword queries into today's AI-powered personalized digital navigation is a positive step towards making the digital world more accessible to the general public.


Healthcare Needs AI, AI Needs Causality

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AI should be built on rigorous knowledge... Note: This is a follow-up to an earlier article on causal machine learning, "AI Needs More Why". There's much to be excited about with artificial intelligence (AI) in healthcare: Google AI is improving the workflow of clinicians with predictive models for diabetic retinopathy [2], many new approaches are achieving expert-level performance in tasks such as classification of skin cancer [3], and others surpassing the capabilities of doctors -- notably the recent report of DeepMind's AI for predicting acute kidney disease, capable of detecting potentially fatal kidney injuries 48 hours before symptoms are recognized by doctors [4]. Yet medical practitioners and researchers at the intersection of machine learning (ML) and medicine are quick to point out these successes are not representative of the more nuanced, non-trivial challenges presented by medical research and clinical applications. These ML success stories (notably all deep learning) are disease prediction problems, learning patterns that map well-defined inputs to well-labeled outputs [5]. Domains where instinctive pattern recognition works powerfully are what psychologist Robin Hogarth termed "kind learning environments" [6].


Automated machine learning or AutoML explained

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The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs). On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how.


Next Generation Machine Learning and Deep Learning Infrastructure

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Spell is a powerful platform for building and managing machine learning projects. Spell takes care of infrastructure, making machine learning projects easier to start, faster to get results, more organized and safer than managing infrastructure on your own. Intuitive tools and simple commands allow you to quickly get started and immediately see the productivity benefits of having infinite computing capacity at your fingertips. Explore your data with Jupyter notebooks, train models on powerful GPUs, create APIs, and automate your entire workflow, Spell makes setting up ML pipelines easy. Run your experiments and models on your own AWS or Google cloud instance, automatically generate records, and keep your data in one place.


Artificial Intelligence (AI) Stats News: AI Augmentation To Create $2.9 Trillion Of Business Value

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The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington