AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What is The Next Big Thing in #AITechnology - Reality, Causality and Mentality? The Next Game is Reality and Causality. The main features involved in the Causal Revolution are disruptive scientific and technological changes, as in: A comprehensive, consistent and coherent causal model of the world for humans and computers. Real AI as Causal Machine Intelligence and Learning involves Causal Logic and Causal Models, Causal Learning, Causal Understanding and Causal Decision, Predictions and Action. Formal Symbolic Logic, as propositional, first/second/higher order predicate logic, as logical relations or part-whole relations, made the engine of #SymbolicAI, and largely blamed for two long "AI winters".

Turning machine learning models into actual applications other people can use is not something that is covered in most AI and Machine Learning Tutorials. In this article, we are going to create an end-to-end AI Sentiment Analysis web application using Gradio and hugging face transformers. According to Wikipedia, Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In simple words, Sentiment Analysis is the ability of Artificial Intelligence to analyze a sentence or block of text and get the emotions behind that sentence or block of text. Gradio is an open-source python library that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or any arbitrary function in just a few lines of code. Gradio makes it very easy for you to build Graphical User Interfaces and deploy machine learning models.

Just damn #AI I reviewed hundreds of papers, checked as many repo, answered thousands of public/private messages. A small #AI community spontaneously grew up here: 15,000 people are now following my posts on LinkedIn. Something that I never expected and that flatters me. To thank you all I'm opening my private #Telegram group with 300 reviews, daily updated: (almost) every posts I have written from 2017 to date. A "repository" to dive in the #AI world and to keep in touch in real time.

XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction with experiments is done via XManager's APIs through Python launch scripts. To get started, install XManager, its prerequisites if needed and follow the tutorial or codelab.ipynb to create and run a launch script. Or, alternatively, a PyPI project is also available.

In a market still filling with fledging silicon chips, Ceremorphic, Inc. has exited stealth and is telling the world about what it calls its patented new ThreadArch multi-thread processor technology that is intended to help improve new supercomputers. Venkat Mattela, the company's founder and CEO of Ceremorphic, calls his latest chip design a Hierarchical Learning Processor (HLP), even though several technology analysts said they recognize it as a system on a chip (SoC) design. The goal of the company is to design, benchmark and market a new kind of ultra-low-power AI training chip. "What we are trying to solve is today – everybody knows how to do higher performance – you can buy an Nvidia machine," Mattela told HPCwire. "Can we have the highest performance in a reliable way? Architecture is how we achieve it," using multiple processors, a multiple logic design and mixing and matching it all.

So far, we only talked about the AI portion, but AI is so much more! You have a model at the core, but how do you make sure that the prediction is able to be generated in real time? You must have a data pipeline, a system that is sitting on infrastructures, and you need to be able to monitor the quality of that system. Do not forget the software engineering that will be required to deploy the system, which is going to be a big portion of the project. Around 80% of your project will be in the creation of the system that is around the machine learning portion.

GANs (generative adversarial networks) are cutting-edge deep generative models that are best known for producing high-resolution, photorealistic photographs. The goal of GANs is to generate random samples from a target data distribution with only a small set of training examples available. This is accomplished by learning two functions: a generator G that maps random input noise to a generated sample, and a discriminator D that attempts to categorize input samples as accurate (i.e., from the training dataset) or fake (i.e., not from the training dataset) (i.e., produced by the generator). Despite its success in enhancing the sample quality of data-driven generative models, GANs' adversarial training adds to instability. Small changes in hyperparameters, as well as randomness in the optimization process, might cause training to fail.

Nicolas Maire is the model of a professional French chef with years of experience and 18 Michelin stars under his belt, a man who dominates his kitchen dispensing nuggets of culinary wisdom as he prepares food for his guests. Today, this kitchen is buried inside a sprawling corporate HQ on the outskirts of Geneva. Mr Maire works for Firmenich, a business with a perfume industry pedigree stretching back to 1895. Firmenich's nose for a new market saw it diversify into food ingredients as the public appetite for alternatives to meat led to a scramble to put plant-based food on supermarket shelves. The company says there is a global market for plant-based meat substitutes of $25bn (£19bn) and believes this will grow to $200bn by 2030.

PREORDER: The highly anticipated Nintendo Switch game Pokémon Legends: Arceus comes out this Friday, Jan. 28. Reserve a copy through Amazon ahead of time and you'll get $5 off the game (from $59.99 to $54.99) plus an exclusive in-game outfit. The reviews are trickling in for Pokémon Legends: Arceus, Game Freak's upcoming action RPG for the Nintendo Switch -- and from the sound of things, it's one of the finest Pokémon games we've gotten in years (albeit a little "messy" at times): "It stumbles often on its way to innovation (it's trying to evolve a nearly 24-year-old series, after all) and brings equal parts brilliance and frustration in its updated systems," writes Polygon's Ryan Gilliam. "But the good bits in Pokémon Legends: Arceus outweigh the bad, rounding out a successful first attempt at an action-centric Pokémon game." Per Steve Watts of GameRant: "Once Pokémon Legends: Arceus finds its stride ... it's the most daring and inventive the series has been in years, breaking apart the staid core and creating something new and exciting from its pieces."

In terms of career opportunities for today's technology professionals, there is an abundance of skills in demand across a wide range of platforms, languages, and methodologies. But technology managers and professionals only have so much precious time outside of their regular jobs/gigs or educational programs. It's a question of where to invest this time and resources. To get a picture of what skills will matter in the 2020s, I canvassed industry experts and leaders to get their takes on what is needed. For starters, the "soft" skills will really matter in the months and years ahead.