Ipsotek was selected by Sydney Trains to deliver an AI-based video analytics solution to security cameras to identify incidences of tunnel and track intrusion at 13 stations across metropolitan Sydney. "Sydney Trains chose Ipsotek, after an extensive comparison of a number of products," said Mark Edmonds, manager of security capability for transport at Sydney Trains. "Ipsotek's proven track record in the AI video analytics space, its partnership with Genetec and its work with Innovate UK's Small Business Research Initiative (SBRI) for railway-focused AI applications, made it a well-deserved choice." Ipsotek's project with Sydney Trains follows the company's previous success in delivering an initial programme of work via the Innovate UK SBRI initiative, for the development of AI video analytics, to enhance the rail experience for passengers and staff in the UK. "As a British SME, competing against some of the industry's big-named companies, we are delighted to have been chosen by Sydney Trains, to deliver this project," said Chris Bishop, sales director APAC & marketing director at Ipsotek.
The world has seen a boom in the field of Artificial Intelligence in the past few years. The major reasons contributing to this is the availability of data and computing power. A lot of research has happened in the field of AI in the last decade and society has witnessed many amazing use cases. In the last decade, AI went mainstream because of the availability of hardware, courses, platforms, big companies taking workshops, etc. What our AI community has achieved in the last decade has set a strong foundation for the future.
We usually think of surveillance cameras as digital eyes, watching over us or watching out for us, depending on your view. But really, they're more like portholes: useful only when someone is looking through them. Sometimes that means a human watching live footage, usually from multiple video feeds. Most surveillance cameras are passive, however. They're there as a deterrence, or to provide evidence if something goes wrong. But this is changing -- and fast.
Artificial intelligence is a powerful asset to your team. Businesses see it as an essential way to remain in ever-changing markets. AI helps fuel knowledge-gathering efforts, visualize data, deliver information in a timely fashion, and dive deeper into thousands of sources that the average person could never find. AI has become an essential tool for business teams, and in this blog, we are outlining why AI is essential for your competitive intelligence (CI) program. Gathering information requires more time and energy than most people or companies can afford to spend.
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master's of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada.
Many creatives have felt rather safe from the robotic takeover that has already touched so many other industries. But with new artificial intelligence tools creeping their way into the creative arts market, it may soon be difficult to tell the difference between humans and robots when it comes to audio creation, video production or writing. Blythe Brumleve talks to Lindsay Watt of Parade.AI and Ayman Husain, director of customer success at Microsoft, about their goals to help the freight industry tackle these complex cloud, computing and capacity challenges and if AI could spell trouble for keeping information factual. Brumleve also reveals her research about the logistics behind the National Football League and what it takes to put on games for millions of fans every week. You can find more Cyberly episodes and recaps for all our live podcasts here.
Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?
A Waymo self-driving car pulls into a parking lot in Mountain View, Calif., on May 8, 2019. A Waymo self-driving car pulls into a parking lot in Mountain View, Calif., on May 8, 2019. It was a modern mystery. In a tiny neighborhood in San Francisco's Richmond District, self-driving Waymo cars have been converging at all hours of the day and night, mystifying neighbors, KPIX reported earlier this week. Most would drive to the dead-end on 15th Avenue, where they then had no choice but to turn around and leave, according to the outlet -- and neighbors have no idea why.
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 Real #AI for Everything and Everybody Platform? The mainstream human-centric AI has some fundamental problems needing for fundamental solutions. First, it is philosophy, or rather lack of any philosophy, and blindly relying on statistics, its processes, algorithms, and inductive inferences, needing a large volume of big data as the "fuel" to train the model for the special tasks of the classifications and the predictions in very specific cases. Second, it is not a scientific AI agreed with the rules, principles, and method of science. Today's AI is failing to deal with reality and its causality and mentality strictly following a scientific method of inquiry depending upon the reciprocal interaction of generalizations (hypothesis, laws, theories, and models) and observable/experimental data. Third, there is no common definition of AI, and each one sees AI in its own way.
Key Takeaways: - New Artificial Intelligence (AI) technology is being integrated into all industries. I have written a few articles regarding the liability of autonomous systems under the United Arab Emirates' (UAE) law, regarding the liability of autonomous systems under the UAE's Civil Code, available remedies, comparing to other regimes, and recommendations for law, policy and ethics. I focused mainly on the liability and regulation of autonomous or Artificial Intelligence (AI) systems under the laws of the UAE, but I also compared the UAE's legal system to other regimes, including the United Kingdom (UK) and the European Union (EU). I concluded that generally speaking, when it comes to AI, the issues are similar across the globe. In the near future, every single one of us will be dealing in some shape or form with an autonomous system or an AI-powered system.