If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
"exploring the humanizing of AI by building a digital brain which can be used as a platform for autonomously animating hyper-realistic digital humans" "I think what will be increasingly important in the digital human space is ethics, as they relate both to the digital human and to the real-life people who may be impacted. From a digital human perspective, companies are essentially birthing entities which, in many cases, are expected to form meaningful connections and relationships with people. So how organizations treat these digital humans--including any decision to dispose of them if they are no longer deemed needed--will increasingly become important. On the flipside, entertainment organizations that are using digital humans run the risk of causing concern of replacing real humans […] and it will be important to clarify how and why digital humans are being used in lieu of the'real' thing." Excerpts from this article: The Virtual Beings Are Arriving Efficient deployment of deep learning models requires specialized neural network architectures to best fit different hardware platforms and efficiency constraints (defined as deployment scenarios).
If I know the entire math logic for machine learning algorithms, but I could not code well, do I stand a chance to enter the data science field? If I just barely know the math behind those machine learning algorithms, but I can code well, am I qualified enough to be a data scientist? I hope I know this answer when I was trying hard to enter into data science before I graduated from university. Some background of mine, I came from a Mathematics background, who does not take a lot of programming courses during university. The programming language I learned in university included R, C, and Matlab.
Node is the first automated machine learning solution designed for platforms that leverage people and company data. Powered by Artificial Intuition technology, Node enables product development teams to create and deploy AI-powered predictive feature sets for both customer-facing and internal use applications, all via a standard API that can be set up in minutes.
In the Learning Systems Group of the Cloud and Information Services Lab (CISL), we research, design and develop state-of-the-art machine learning algorithms, tools and systems. Our mission is to contribute to the democratization of machine learning through research. To do so, we collaborate with Microsoft Research and the academic community. We actively engage in open source software development. Our work directly informs and shapes products such as Azure ML, Azure Data Lake and Microsoft R Server.
In this blog post, we explore a functional paradigm for implementing reinforcement learning (RL) algorithms. The paradigm will be that developers write the numerics of their algorithm as independent, pure functions, and then use a library to compile them into policies that can be trained at scale. We share how these ideas were implemented in RLlib's policy builder API, eliminating thousands of lines of "glue" code and bringing support for Keras and TensorFlow 2.0. One of the key ideas behind functional programming is that programs can be composed largely of pure functions, i.e., functions whose outputs are entirely determined by their inputs. Here less is more: by imposing restrictions on what functions can do, we gain the ability to more easily reason about and manipulate their execution.
The 1.5 meter, silvery gray velociraptor lunges forward, interrupting the flight of the tennis ball with its head before the ball can get to the soccer net at the end of the gym. Its tail stretches out, stopping another ball. It pivots, somewhat clumsily, and runs three steps in the other direction to intercept a third ball. Robots building Teslas aren't as sophisticated as AI velociraptors that tend goals It's been doing this for an hour, running back and forth as a trio of tennis ball machines toss yellow balls in various loopy ways toward the net. It's a game that its creators have invented to rapidly improve its coordination. But then it stops trying to intercept the balls, although it still twitches toward them.
The Machine Learning Summit is designed for everyone from data scientist to business professionals. If you've ever been curious about artificial intelligence and machine learning, whether you're just getting started on your machine learning journey or already a machine learning practitioner, this Summit will provide you with knowledge of what's on the horizon for machine learning. To attend the Machine Learning Summit, purchase a ticket to AWS re:Invent 2019. Once reserved seating opens in the fall, you will be able to register for a seat.
This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn't a particularly novel architecture – it's architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we'll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner-workings of transformers, and how they've evolved since the original paper. My hope is that this visual language will hopefully make it easier to explain later Transformer-based models as their inner-workings continue to evolve.
H2O is a fully open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, deep learning, and many more. H2O also has an industry-leading AutoML functionality (available in H2O 3.14) that automates the process of building a large number of models, to find the "best" model without any prior knowledge or effort by the Data Scientist. H2O AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. Some of the important features of H2O's AutoML are: H2O's AutoML can also be a helpful tool for the novice as well as advanced users.
LOS ANGELES, CA, Oct. 20, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE – iPR Software, the leader in Online Newsrooms, Digital Publishing, Digital Asset Management (DAM) solutions, and customized integrated solutions, announced its largest technology rollout to date at Public Relations Society of America's International Conference in San Diego, California. With the launch of "Metatron," iPR Software's new application empowers Artificial Intelligence (AI) cloud capabilities as well as integrating the power of machine learning into DAM and customized software platforms to increase productivity and corporate asset sharing across multiple customer ecosystems. This latest software release further advances the company's vision for clients to publish their news and information to Traditional and Social media channels and better engage their B2B & B2C audiences while increasing traffic to their branded media and corporate assets. Leading organization's today are utilizing cloud applications to access the latest technology with encryption algorithms they can securely manage, publish, and share rich branded media content. Metatron introduces core, cloud-based software features that enable customers to securely publish and share key digital media and corporate assets, target practical enterprise use cases, increase workflow efficiencies, and automate mundane tasks to reduce data and storage errors.