ROI of chat bots. AI: made in Canada. Top 10 AI podcasts. http://cognitionx.com/news-briefing/.
Check out this 230-page government commissioned review on industrial digitalisation. The report argues that the UK must prepare for the ‘fourth industrial revolution’.
As to be expected, AI features prominently. We’re excited to see what will come of their suggestion to create 12 digital innovation hubs where startups would work with universities and established firms such as Siemens. Here are some highlights:
Britain’s manufacturing sector could unlock £455B over the next decade if they play their cards right and innovate at scale
A deal between government and industry could help bring about 175,000 highly skilled, better-paid jobs
Their analysis identified a £22.4B opportunity for pharmaceuticals over the next 10 years through the adoption of ‘currently known digital technologies’
You can check out the report here. For news coverage, check out this article from the Guardian, as well as this one from the BBC.
According to CEB, a research and advisory company now part of Gartner, 40% of roles that exist today will be significantly different in five years’ time. Helping employees change direction without leaving their employer is in everyone’s interests. Could the pattern-matching capabilities of AI put underused human intelligence to better use? But even as employers seek to plug gaps by hiring from outside, studies point to workers being pigeonholed, rather than helped to explore new careers within their organisations.
Orange is one of several French employers that use AI developed by Clustree, a Paris start-up, to help people move within their companies. Employees submit a CV describing their experience, pastimes and aspirations. Details can reveal surprising abilities, according to Véronique Biecques, director of recruitment at Orange. “What you do in your spare time sometimes says more about you than your job,” she says. Studies suggest that we view those whom we resemble in background and tastes as more able, but machine intelligence is — at least in theory — neutral.
According to Suhas Uliyar, Oracle’s VP of Bots and AI, there is a rapid adoption of AI chatbot technology among enterprise customers. He argues that chat bots provide strong ROI by automating some of the repetitive end-user interactions, leaving the customer service agents to focus on high-value and high priority customers.
Also, an AI Report from Woodside Capital Partners indicates that “By 2022, most sales and marketing communications, whether by professionals or customers, will be between humans and AI. Whether a personal assistant, a support bot, or a lead evaluation algorithm, AI will consume virtually all routine sales and marketing tasks…and be invaluable in increasing back-office productivity.”
Speaking at a major AI event in Toronto, Canadian Prime Minister Justin Trudeau demonstrated an impressive enthusiasm for AI and machine learning, at one point even taking a stab at describing the concept of deep reinforcement learning, an approach that lets computers learn to do complex things that can’t be programmed manually (see “10 Breakthrough Technologies 2017: Reinforcement Learning”).
Trudeau addressed concerns about the technology’s path, including ethical risks and unknown consequences. Machine-learning algorithms that learn through experience often cannot be interrogated, he pointed out. The opacity of AI algorithms is, in fact, a growing concern for many AI researchers (see “The Dark Secret at the Heart of AI”). He suggested that whether AI continues to be made in Canada or not, progress is unlikely to slow down. “The pace of change has never been so fast,” he said, “but it will also never be this slow again.”
There are a bunch of really great podcasts out there which provide us (and can provide you) with insights into the latest and greatest innovations in the world of AI. Whether it’s research papers, cool companies, or AI superstars, this curated list of podcasts from Analytics India Magazine will have you covered.
Just in time for Halloween, a research team from the MIT Media Lab’s Scalable Cooperation group has introduced Shelley: the world’s first artificial intelligence-human horror story collaboration.
Shelley, named for English writer Mary Shelley — best known as the author of “Frankenstein: or, the Modern Prometheus” — is a deep-learning powered artificial intelligence (AI) system that was trained on over 140,000 horror stories on Reddit’s infamous r/nosleep subreddit. She lives on Twitter, where every hour, @shelley_ai tweets out the beginning of a new horror story and the hashtag #yourturn to invite a human collaborator. “Shelley is a combination of a multi-layer recurrent neural network and an online learning algorithm that learns from crowd’s feedback over time,” explains Pinar Yanardag, the project’s lead researcher. “The more collaboration Shelley gets from people, the more and scarier stories she will write.”
This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration.
The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. In this paper, the authors suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense).
They argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. They review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.
“Every year companies lose billions of dollars due to misplaced items and faulty inventory records in their warehouses,” says Fadel Adib, an assistant professor of media, arts and sciences at Massachusetts Institute of Technology.
Two drones can do the work of 100 humans over the same time period, according to supply chain specialist, Argon Consulting. This means they can do several tours of a warehouse – even at night – compare results, identify discrepancies, and build up a much more accurate picture much more quickly. Drone makers claim scanning accuracy of close to 100%. Matt Yearling, chief executive of Pinc, one of the firms offering such aerial robots, says they can save warehousing and logistics companies millions of dollars.
Article to Share With Your Less Data Savvy Friends
Check out Botnik. This Seattle-based startup is the comedic offspring of Jamie Brew, previously a head writer for ClickHole, a satirical website connected to The Onion, and Bob Mankoff, cartoon and humour editor of Esquire and former cartoon editor of The New Yorker.
Botnik builds a “predictive keyboard” of words taken from various sources — beauty ads, nature shows, famous poets, dialogue from “Seinfeld” episodes and even combinations of sources, including the unlikely
triumvirate that is Beowulf/Maya Angelou/forklift manual. Botnik users can enter their own source to create a keyboard. The program analyes the sentences in the source to build a model of which words are likely to follow each other. Then a user calls up a keyboard and starts creating her or his own computer-assisted quips. As each word is selected, the Botnik app churns out 18 choices for the next word based on the highest probability of continuing the sequence.