AI News, Data Science Community
- On Wednesday, March 7, 2018
- By Read More
Data Science Community
We continue to engage with developers to help us in making driving in London better, with innovative solutions to traffic, road disruption and planned works information through apps created from our open data. As part of our engagement with the developer community we held an Urban Traffic Data Hackathon on 14-15 November.
Supported by our Roads Space Management team, the event was planned in order to give us the opportunity to engage directly with developers to work on creative and innovative solutions to the challenges on London’s roads. In putting the Hackathon together, we worked with Data Science London (DSL), the largest data science community in Europe, and arranged for data scientists and innovators who are members of DSL to take part in the event.
The run up to the event included a pre briefing session with 380 attendees to share our aims and information about the data sets we were making available. The data sets were received with great interest by the group, and the numbers wishing to attend the weekend were so high they had to be limited to the venue capacity.
This continues to open up new ways to explore solutions to London’s challenges. If you attended the Hackathon and have any comments or feedback on the event itself, or if you have any other questions or comments on TfL’s open data and unified API in general, we’d love to hear from you – let us know your thoughts in the comments section below.
Data-Directed Road Repairs Could Save Money and Lives
The United Nations Global Pulse, an innovation initiative on big data and data science, and Western Digital recently announced the winners of the Data for Climate Action Challenge (D4CA) at the Data Innovation: Generating Climate Solutions event during the United Nations climate change conference (COP23) in Bonn, Germany.
An unprecedented open innovation challenge to harness data science and big data from the private sector to fight climate change, D4CA was launched earlier this year and called on innovators, scientists, and climate experts to use data to accelerate climate solutions.
Access to large amounts of data – anonymized and aggregated to protect privacy – accelerates the ability to spot connections, gain insight and develop predictive algorithms that can provide more precise direction and decisions.
What we’re doing is we’re trying to do is work out how best to fortify the road network from flooding in order to maximise accessibility during flood events.
We obtained flooding data from a company called Fathom, they gave us a high resolution, gridded map, that tells us how much and how likely each grid cell on that map will flood under different scenarios.
WBL: Could this sort of dataset be used as a proxy for development, trade, population movements, things like that? Caleb: If you know one area of the country is responsible for producing a lot of food, losing access to that part of the country would be devastating.
Using the computational models we’ve built, we think this can be applied to lots of areas to help make better decisions when it comes to protecting road networks against natural disasters and climate change.
This content is produced by WIRED Brand Lab in collaboration with Western Digital Corporation Data Makes Possible will be following the winners as they work to implement their solutions and bring real change to our world, and we’ll be publishing interviews with the thematic and data visualization winners throughout January and February.
The road to artificial intelligence: A case of data over theory
This article is usually available only to subscribers but is being made free to view thanks to sponsorship from Ocado IN the summer of 1956, a remarkable collection of scientists and engineers gathered at Dartmouth College in Hanover, New Hampshire.
They did not lack in ambition, writing in their funding application: “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
Their wish list was “to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves”.
In 2016, we can ask a computer questions, sit back while semi-autonomous cars negotiate traffic, and use smartphones to translate speech or printed text across most languages.
At the Dartmouth conference, and at various meetings that followed it, the defining goals for the field were already clear: machine translation, computer vision, text understanding, speech recognition, control of robots and machine learning.
They expected to generate intelligent behaviour by first creating a mathematical model of how we might process speech, text or images, and then by implementing that model in the form of a computer program, perhaps one that would reason logically about those tasks.
This pragmatic attitude produced success in speech recognition, machine translation and simple computer vision tasks such as recognising handwritten digits.
Every time you drag an email into the spam folder, you enable it to estimate the probability that messages from a given recipient or containing a given word are unwanted.
But when these ideas are applied on a very large scale, something surprising seems to happen: machines start doing things that would be difficult to program directly, like being able to complete sentences, predict our next click, or recommend a product.
While each of these mechanisms is simple enough that we might call it a statistical hack, when we deploy many of them simultaneously in complex software, and feed them with millions of examples, the result might look like highly adaptive behaviour that feels intelligent to us.
It has been a very humbling and important lesson for AI researchers: that simple statistical tricks, combined with vast amounts of data, have delivered the kind of behaviour that had eluded its best theoreticians for decades.
Thanks to machine learning and the availability of vast data sets, AI has finally been able to produce usable vision, speech, translation and question-answering systems.
“Modern artificial intelligence is a brilliant and powerful technology, but also a fundamentally disruptive one“ One important step has been to recognise that valuable data can be found freely “in the wild”, generated as a byproduct of various activities – some as mundane as sharing a tweet or adding a smiley under a blog post.
Engineers and entrepreneurs have also invented a variety of ways to elicit and collect additional data, such as asking users to accept a cookie, tag friends in images, rate a product or play a location-based game centred on finding monsters in the street.
Every time you access the internet to read the news, do a search, buy something, play a game, or check your email, bank balance or social media feed, you interact with this infrastructure.
But the quality of those predictions will depend on subtle design choices and on the way the information used to train it is collected, which creates a very real risk of implicit and unintended discrimination.
The emergence of internet crowdsourcing allows businesses to automatically outsource micro-tasks that require human intelligence, by posting them on websites or apps where workers can choose the tasks they want to accept.
“Coming soon: the second half of our Instant Expert on artificial intelligence, in which Nello Cristianini delves deeper into the technology that allows machines to learn“ Artificial intelligence has come a long way from its early days in academic laboratories.
The machine-learning paradigm has been effective in addressing many areas like vision and speech processing, and it is likely that future AI will also find a way to integrate some top-down reasoning methods descended from earlier approaches.
As our AI efforts continue to open up new possibilities, we can imagine seamless conversations with machines, fluent real-time translation of speech, and many useful ways to automate our houses and cars.
Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants.
Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.
A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers.
Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements.
Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.
Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business.
Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.
Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.
“This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.
No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.
At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT;
One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey.
They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.
Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs.
Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’” CIOs have always needed the ability to educate and influence other leaders that they don’t directly control.
There is too much volatility and uncertainty for them to rely on their intuition or past experiences.” Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid.
Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs.
In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.
“If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.” Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.
For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building.
they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.
The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.
- On Monday, August 19, 2019
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