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) …
Each month, the patent lawyers at the Electronic Frontier Foundation shine a spotlight on one particular patent they believe is a drag on innovation. This month, they're looking at one of the fastest-growing sectors of technology: machine learning and artificial intelligence. EFF lawyer Daniel Nazer has picked out an artificial intelligence patent belonging to Hampton Creek, a San Francisco food-tech company that markets products under the brand name "just." Nazer acknowledges that Hampton Creek's patent isn't as bad as some of the other ones highlighted in the EFF Stupid Patent series, but it's worth pointing out because of the serious problems it could create for innovation in machine learning.
We must think now about the consequences that AI will have very shortly in our lives – changing work, society, economics and more. There's some encouragement that at last governments around the world are waking up to the size of some of the societal questions that AI innovation will ask of us. There is nothing to say that our economic system requires a constant amount of human labour; we should treat the reduction in need for labour as a huge positive, rather than seeking to generate jobs simply to keep people occupied. Shorter working weeks would give more people a stake in employment, and also give them more time to do other things with their lives.
AI – which Margaris prefers to call "the influence of machine learning or deep learning" – is starting to be felt across the insurtech, fintech and associated industries, he said. "AI, through machine learning and deep learning, will eventually become the entrepreneur of the future--and we humans need to compete against it." A company still needs to have a compelling business case that attracts clients, but AI, machine learning and deep learning for sure will be part of the equation to compete successfully in their space." Margaris has reiterated what has been the most important technology lesson learned over the past four decades, and continues to be the lesson going forward with each new technology wave.
These were some of the issues that emerged this spring when Xconomy brought together some of San Diego's most-prominent tech and life sciences leaders for a dinner discussion about the risks and opportunities in the convergence of AI and healthcare. Interest in healthcare AI runs high in San Diego, which has a well-established life sciences cluster and is home to two genome sequencing giants: Illumina and the life sciences solutions group of Thermo Fisher Scientific (NYSE: TMO). The kickoff question: Is there a proven business model for startups that are applying innovations in machine learning in the life sciences? Bruce V. Bigelow is the editor of Xconomy San Diego.
I took my first "business machines" class in high school in 1991, attended my first computer science class in 1994 (learning Pascal), and moved to Silicon Valley in 1997 after Cisco converted my internship into a permanent position. The eight I'm covering in this article include desktop operating systems, web browsers, networking, social networks, mobile apps, Internet of Things, cloud computing, and artificial intelligence. In the social network space, development is completely centralized by the owners of the platforms (Twitter, Facebook, etc.) A technology with a low barrier to entry and decentralized platform development has the greatest potential for future impact.
Taipei, May 29 (CNA) Computex Taipei 2017, a global trade show for information communication technologies (ITC) and the Internet of Things (IoT), will focus on five themes, including artificial intelligence (AI), robotics and IoT applications from May 30 to June 3, according to the organizers. While evolving in sync with global ICT industry trends, Computex 2017 positions itself as "Building Global Technology Ecosystems," focusing on such themes as AI and Robotics, IoT Applications, Innovations and Startups, Business Solutions, and Gaming and VR (virtual reality), said the Taipei Computer Association, one of the organizers. Exhibits and the high-profile CPX series forums will take place at Taipei World Trader Center, Nangang Exhibition Hall and Taipei International Convention Center, respectively. This year, for the first time ever American electric automobile manufacturer Tesla and computer maker Dell will join 1,600 businesses from around the world with booths at the annual ICT trade show in Taipei, the Taipei Computer Association said.
Freedom to innovate THOSE THAT DO EXPECT MORE: Opportunity to develop standards that others follow Expanded opportunities for trusted partnerships 6. www.accenture.com/bankingtechvision DESIGN FOR HUMANS CUSTOMER JOURNEYS NOW RUN INSIDE AND OUTSIDE THE BANK Digital banking models, such as ecosystem platforms and channels not owned by banks, will bring consumers from outside the sphere of the bank's knowledge. WORKFORCE MARKETPLACE ON-DEMAND TALENT AS A TRUE BANKING INNOVATION Create an agile workforce to access sought-after skills, knowledge and experience as-needed for more flexible ways of working. DESIGN FOR HUMANS CUSTOMER JOURNEYS NOW RUN INSIDE AND OUTSIDE THE BANK Digital banking models, such as ecosystem platforms and channels not owned by banks, will bring consumers from outside the sphere of the bank's knowledge. WORKFORCE MARKETPLACE ON-DEMAND TALENT AS A TRUE BANKING INNOVATION Create an agile workforce to access sought-after skills, knowledge and experience as-needed for more flexible ways of working.
With insurance, you never get a reward, there's just a cost," says Filiippo Sanesi, head of research and partner management at Startupbootcamp InsurTech, an accelerator for insurance startups based in London. The billions of images around the world mean that not only is the value from performing this task with AI enormous, but it's also possible to train the AI because you've got these mountains of data," Tractable's co-founder and chief executive Alex Dalyac, tells Verdict. "We're not just distributing products; we're working towards building accurate, personalised insurance products around people's lives." "We're not just distributing products; we're working towards building accurate, personalised insurance products around people's lives," says Hugh.
Artificial intelligence came alive in the '80s with many startups, governments, and large enterprises deploying new systems that executed tasks typically performed by human experts. These were largely rule-based systems that encoded behaviors in rules instead of using the strict procedural logic of traditional programming languages. Then, as memory became more affordable, systems were able to handle much more computationally intense tasks, such as machine learning, planning and scheduling, and natural language understanding. Now in the age of big data, many believe AI has completely changed the tech landscape, but in some ways, as the Talking Heads song goes, it's the "same as it ever was." What remains the same are the core elements of an intelligent application.
For Deep Knowledge Ventures, the Hong Kong-based venture firm that added a machine learning algorithm named VITAL to its board in 2014, it was about adding a tool to analyse market data around investment opportunities. For global professional service firms experimenting in this space, machine learning could allow deeper and faster document analysis. And though you may not think you are competing with Silicon Valley salaries for talent, you are if you want great people: a great data scientist can easily be 50 times more valuable than a competent one, which means that both hiring and retaining them can be pricey. As the machine learning ecosystem evolves, companies will find interesting ways to combine in-house industry experience with a range of off-the-shelf tools and open source algorithms to create highly-customised decision-support tools.