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) …
Artificial intelligence is already impacting our lives. And the use of AI for social functioning is on an all-time high. Be it getting riding directions through our smartphone or getting daily reminders by using our health system to extend our workouts; all these are manifestations of how artificial talent is altering the way we function. What is often much less understood is the vast function synthetic brain can play in the social sector. The Artificial Intelligence for social good can probably assist in solving some of the country's most pressing problems. As a count number of facts, it can contribute in some way or every other to tackling and addressing all of the United Nation's Sustainable Development Goals, supporting large sections of the populace in both growing and developed countries. AI is already helping in several real-life situations, from assisting blind humans in navigating and diagnosing cancer to identify sexual harassment victims and helping with catastrophe relief. Let us take a look briefly at integral social domains where AI can be carried out effectively.
Investment in warehouse robotics technology startups clocked in at $381 million in the first quarter of 2020, up 57% from the same period in 2019. The study examines venture capital (VC) investment levels, trends, new business models and notable startups within last-mile delivery, freight, warehousing and enterprise supply chain management. While warehouse robotics investment exploded during this period, overall investment in supply chain tech declined in the first quarter of 2020. Supply chain technology companies raised $1 billion in VC across 59 deals in North America and Europe, a decline from $1.4 billion in Q1 2019. The PitchBook report comes one month after the firm conducted a survey in partnership with last month's Collision From Home conference examining investor sentiment toward technology in a post-COVID-19 world.
We live in an age of rapid AI innovation and progress. Yet even as academics and researchers make astonishing advancements, demonstrating real business value and positive return on investment is challenging. Developing cutting edge AI applications based on machine learning models integrated with existing business software is a common challenge. This article discusses a few of the core pain points and strategies to address them. The first challenge most organizations encounter is the increased complexity of preparing data and dataset management.
A lot of the people who are working at the many AI chip startups have a long history in processor development in the datacenter, and that is certainly true of the folks who founded SambaNova Systems. And this is a fortunate thing because these people can leverage some of the good ideas they know worked when commercializing a new technology and avoid some of the big mistakes their former employers sometimes made. At our recent The Next AI Platform event, we sat down with Rodrigo Liang, co-founder and chief executive officer of SambaNova Systems, which is one of the upstart custom AI chip producers vying for attention and budget dollars. SambaNova was founded in 2017 by a bunch of ex-Sun Microsystems techies as well as a few from Stanford University, which is of course where Sun itself was born in 1982. The co-founders include Kunle Olukotun and Chris Ré, professors at Stanford, with Olukotun being the leader of the Hydra chip multiprocessor research project and sometimes known as the father of the multicore processor.
Think critically about whether you need to apply deep-learning to your datasets. Deep Learning, one of the "hottest" things in AI, has a way of seeping into popular culture as this mysterious, software that can make seemingly amazing classifications at human-level accuracy in Computer Vision, speech recognition, or play games like Go, recommend our favorite movies, and the like. But deep learning has crucial pitfalls, when it drives cars that sadly, more than once, have injured or killed their drivers or pedestrians because of silly image-recognition mistakes. Or, when deep learning is used for face-recognition ––something that clearly has adverse effects on people of color, LGBT, and other marginalized groups –– and if deep learning's face-prediction is used by institutions of power with a history of racism, LGBT-phobia, and tossed back and forth between private companies and governments –– deep-learning's pitfalls become frighteningly magnified. Another example is when Facebook's deep-learning neural translation machine led to the illegal arrest of a Palestinian man because of a post he made, at the end of 2017.
Every day there is something new going on in the world of AWS Machine Learning--from launches to new use cases like posture detection to interactive trainings like the AWS Power Hour: Machine Learning on Twitch. Check back at the end of each month for the latest roundup. As models become more sophisticated, AWS customers are increasingly applying machine learning (ML) prediction to video content, whether that's in media and entertainment, autonomous driving, or more. Want more news about developments in ML? Check out the following stories: Also, if you missed it, see the Amazon Augmented AI (Amazon A2I) Tech Talk to learn how you can implement human reviews to review your ML predictions from Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon SageMaker, and other AWS AI/ ML services. See you next month for more on AWS ML! Laura Jones is a product marketing lead for AWS AI/ML where she focuses on sharing the stories of AWS's customers and educating organizations on the impact of machine learning.
Silicon Valley Bank, which has helped fund more than 30,000 startups, yesterday released a report on "The Future of Robotics: An Inside View on Innovation in Robotics." It described trends in production, business models, and the adoption of robotics reflecting the increasing maturity of Industry 4.0. The report also addressed concerns about automation displacing jobs and public-policy reactions. Overall, the free Silicon Valley Bank (SVB) report (download PDF) was cautiously optimistic about the prospects for industrial automation. It cited rising U.S. productivity, maturing technologies and suppliers supporting a variety of applications, and a steady climb for robotics deployments, particularly in Asia.
The video below contains the first glimpse at the upcoming electric GMC Hummer. The preview video is short, full of nonsense buzzwords, but still telling. It's clear GM identified two main competitors against the upcoming Hummer: The Ford Bronco and Tesla Cybertruck. The Hummer EV was announced pre-COVID 19 during the Super Bowl. At the time, GM promised it would feature 1,000 HP from the electric powertrain.
The federal government on Tuesday asked a federal judge to sentence Anthony Levandowski to 27 months in prison for theft of trade secrets. In March, Levandowski pleaded guilty to stealing a single confidential document related to Google's self-driving technology on his way out the door to his new startup. That startup was quickly acquired by Uber, triggering a titanic legal battle between the companies that was settled in 2018. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast.