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) …
Stephen Foskett discusses the practicalities involved in packaging, deploying, and operating AI models with Manasi Vartak of Verta. Deploying an AI model in production is a challenge, just like it was in the past with software. Once a company has an AI model to deploy, they must validate its results, create scaffolding code to make it consumable, optimize the data pipelines, instrument it, and assign operators. This is what Manasi and Verta have developed, and the world of AIOps parallels that of DevOps but with some unique twists. The data component of AI models presents a unique challenge not found in some other enterprise applications, and it is important to continually test the model to ensure that it hasn't drifted off target as data changes. Previously, training models was the main challenge for AI, but now it's all about getting things into production. That's why we started this podcast and why we created AI Field Day! This episode features: Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett Manasi Vartak, CEO and Founder of Verta (@VertaAI). Find Manasi on Twitter at @DataCereal Date: 10/20/2020 Tags: @SFoskett, @DataCereal, @VertaAI
Previously an academic, Vivienne Ming initially regretted her decision to become an entrepreneur. Her first project combined neuroscience and artificial intelligence and education. Thinking she could change the world, she created amazing technologies, that everyone she demonstrated to loved. However, investors didn't see the opportunity and didn't want to work with her. Instead, they wanted to buy the entire product and appoint their own CEO's.
What's New: At the inaugural all.ai INAI is an initiative to apply AI to population scale problems in the Indian context, with a focus on identifying and solving challenges in the healthcare and smart mobility segments through strong ecosystem collaboration. "With its unique strengths of talent, technology, data availability, and the potential for population-scale AI adoption, India has this tremendous opportunity to lead human-centric applications and democratize AI for the world. Our aspiration is to make AI synonymous with India as we strive to achieve the true potential of AI in critical segments like healthcare, smart mobility and the future of work by advancing innovation, research, technology and skills. The launch of the Applied AI Research Center, initiatives to train students on AI readiness skills and the all.ai
Historians and nostalgic residents alike take an interest in how cities were constructed and how they developed -- and now there's a tool for that. Google AI recently launched the open-source browser-based toolset "rǝ," which was created to enable the exploration of city transitions from 1800 to 2000 virtually in a three-dimensional view. Google AI says the name rǝ is pronounced as "re-turn" and derives its meaning from "reconstruction, research, recreation and remembering." This scalable system runs on Google Cloud and Kubernetes and reconstructs cities from historical maps and photos. There are three main components to the toolset. Warper is a crowdsourcing platform,where users can upload photos of historical print maps and georectify them to match real world coordinates.
Covid-19 has severely impacted the whole world socially, economically and health-wise. And as uncertainty grips the post coronavirus phase and the "new normal" phrase lingers on, one thing is for sure from many debates and discussions--neither the business models nor workplace dynamics will remain the same as before. Already, innovative tech giant companies like Google have developed interactive maps to help track both local and global cases of the novel coronavirus. These maps feature statistics of verified cases, recoveries, deaths as well as predictions of how the crisis is likely to unfold in the near future. Chances are that you're relying on one of these kinds of maps to stay updated now and then with the latest on the pandemic.
Key context: Schmidt has previously warned about the encroaching command of China in the AI sphere, particularly with its military buildup and "high tech authoritarianism." To counter the threat, Schmidt said Thursday that the U.S. should invest more in research, ethics and AI infrastructure and partner with countries such as Canada, the United Kingdom, Israel and Japan. He said broad consensus exists in the West on AI ethics, but those would probably contrast with standards developed in China. Having the United States and its partners lead the charge on ethical standards will be crucial in ensuring they reflect "human values," Schmidt said. "China is simply too big," he said.
Following a conversation and transcribing it precisely is one of the biggest challenges in artificial intelligence (AI) research. For the first time now, researchers of Karlsruhe Institute of Technology (KIT) have succeeded in developing a computer system that outperforms humans in recognizing such spontaneously spoken language with minimum latency. This is reported on arXiv.org. "When people talk to each other, there are stops, stutterings, hesitations, such as'er' or'hmmm,' laughs and coughs," says Alex Waibel, Professor for Informatics at KIT. "Often, words are pronounced unclearly." This makes it difficult even for people to make accurate notes of a conversation.
Artificial intelligence is changing the world of image editing and manipulation, and Adobe doesn't want to be left behind. Today, the company is releasing an update to Photoshop version 22.0 that comes with a host of AI-powered features, some new, some already shared with the public. These include a sky replacement tool, improved AI edge selection, and -- the star of the show -- a suite of image-editing tools that Adobe calls "neural filters." These filters include a number of simple overlays and effects but also tools that allow for deeper edits, particularly to portraits. With neural filters, Photoshop can adjust a subject's age and facial expression, amplifying or reducing feelings like "joy," "surprise," or "anger" with simple sliders.
Building artificial intelligence (AI) models is not like building software. It requires a constant'test and learn' approach. Algorithms are continually learning and data is being refined -- and as much relevant, high-quality data as possible is key. Data labelling is an integral part of data pre-processing for machine learning. If you're training a system to identify animals in images, for example, you might provide it with thousands of images of various animals from which to learn the common features of each, which would eventually enable it to identify animals in unlabelled images.
A global survey of more than 3,000 managers, as well as interviews with executives and scholars, has reported that a majority of companies are developing artificial intelligence (AI) capabilities but have yet to gain significant financial benefits from their efforts. The survey, published in the Expanding AI's impact with organisational learning study from Boston Consulting Group in partnership with MIT Sloan Management Review, found that just one in 10 companies generates significant financial benefits from AI. The study notes that the adoption of AI across industries is increasing, and more companies perceive that AI drives both strategic opportunity and risk. The researchers found that 57% of companies report having AI pilots or have deployed AI. This is a significant increase from 2018, when 44% of companies said they were piloting or deploying AI.