INDUSTRY


Singapore will use driverless buses on roads from 2022

Daily Mail

Driverless buses will appear on some'quiet' roads in Singapore from 2022 as part of plans to improve mobility in the land-scarce city-state, its transport minister has announced. Singapore has so far avoided the massive traffic jams that choke other Asian cities like Manila and Jakarta by imposing road tolls, spending massively on public transport and becoming one of the world's most expensive places to own a car. It now plans to embrace self-driving technology to further reduce reliance on cars and improve how people get around. Driverless buses will appear on some roads in Singapore from 2022 as part of plans to improve mobility in the land-scarce city-state, its transport minister has announced. The buses will be deployed in three new suburban towns -Punggol, Tengah and the Jurong Innovation District.


Are Banks and Credit Unions Prepared for a New Mobile Era?

#artificialintelligence

The financial services industry must respond to a mobile marketplace that has a new demographic profile, new usage patterns and new expectations around AI, IoT and other digital technologies. After years of strong mobile growth being driven by younger demographic segments, the majority of recent, more modest growth can be attributed to the 55 and older generation. In fact, consumers in the 55 age group have a three-year compound annual growth rate of nearly 8% compared to only 2% for the 18 to 34 segment, according to a study from Deloitte. While the rate of growth in ownership of smartphones has tapered off, exciting technologies are beginning to stimulate the imagination of the mobile consumer, including artificial intelligence (AI), machine learning, virtual reality (VR) and augmented reality (AR), the internet of things (IoT), 5G, and the integration with other non-phone devices (watches, glasses, tablets, etc.). At the end of the day, the banking industry must stay in front of these trends, offering consumers the experience they expect on the platform(s) they prefer.


Tesla's South Australian battery to begin final testing

ZDNet

The world's largest lithium-ion battery being built in South Australia to store renewable energy is about to enter final testing. State Premier Jay Weatherill has said Elon Musk's Tesla has finished installing the battery powerpacks at Jamestown, in the state's mid-north, where they are linked to an adjacent wind farm. Weatherill said the 100-megawatt battery will now be energised and tested to ensure it meets all energy market and state government regulatory requirements, and will be up and running in time for the Southern Hemisphere's summer season. When first announced in July, the battery came with a guarantee from Musk that it would be working within 100 days of the grid interconnection agreement being signed, or it would be free for the South Australian government. The 100MW/129MWh battery is expected to provide backup and stability services through energy storage to the South Australian grid.


How to Overcome the Digital Paradox

#artificialintelligence

The digital economy is accelerating rapidly, yet there is uneven progress as enterprises seek to take advantage of the digital opportunity. In my experience, companies today fall into two categories: those that are surviving, and those that are thriving. The companies that are surviving are perhaps making incremental changes to their business, but largely going about things with a business-as-usual approach typically challenged by siloed organizations, tactical plans, limited expertise and insights. Then there are those that are thriving. These organizations are striving to reimagine their businesses and are neither content nor complacent--they know they can always be more efficient, competitive and relevant.


[D] How to build a Portfolio as a Machine Learning/Data Science Engineer in industry ? • r/MachineLearning

@machinelearnbot

I'd like to add a bit to this discussion, if I may... Since ML people come from many places, but chiefly statistics (incl. The former prefer "stream-of-consciousness" work, where they get ideas and test them, play around for a bit until they get something that works well for them... and then keep the notebook in that format, usually due to lack of time/resources for this "finished" project. This generally makes it hard for other people to easily wrap their heads around what you've done by themselves (ie without a walk-through). This philosophy is more or less the basis of the R language and Jupyter-style notebooks: easy for experimentation and results are immediate. I use this approach when exploring new tasks.


Driving IoT Enabled Predictive Maintenance - Cloudera VISION

#artificialintelligence

Powered by sensors, connectivity and smart machines, the Internet of Things (IoT) is reshaping the manufacturing and industrial processes, effectively changing the paradigm from'repair and replace' to more of'predict and prevent'. For asset heavy industries, unplanned equipment downtime can mean big losses in revenues and productivity. For example some of the leading automotive manufacturers estimate that unplanned downtime can cost them as much as $15,000 – $20,000 per minute and a single downtime event can cost approximately $2 million. Given the business impact, it is not surprising that these industries have been focusing on driving predictive maintenance to minimize downtime and losses. Manufacturers and organizations in other asset heavy verticals cannot afford to wait till a machine or equipment breaks down in order to figure out what went wrong.


AI won't peak at human intelligence

#artificialintelligence

Over the past few years, AI has dominated news cycles and captured the imagination of entrepreneurs, investors, and consumers alike. We can see the potential: self-driving transportation on-demand, robotic assistants in the home, and Amazon Echo version 14.0 to do things the human mind could never even contemplate. That future isn't far off -- a decade or so, maybe. But as much as we talk and read about AI, many of us still think about it in the wrong way. People compare artificial intelligence to human intelligence too much and often see human intellect as the end goal for AI.


Uber's 'disruption' is far from benign - but it's not too big to ban Abi Wilkinson

The Guardian

Uber is one of those companies that seems to take pride in upsetting the status quo. Its cheerleaders claim the minicab app is a shining example of "disruptive innovation" – where entrepreneurs change entire industries by thinking outside the box. Critics contend that Uber's business model is actually pretty traditional. The only major difference is scale, and the use of a high-tech booking system. And while the app booking system is certainly convenient, it's far from unique to Uber.


Airbus is looking towards a future of pilotless planes

The Independent

Airbus is looking to develop autonomous aircraft and technologies that will allow a single pilot to operate commercial jetliners, helping cut costs for carriers, chief technology officer Paul Eremenko said. "The more disruptive approach is to say maybe we can reduce the crew needs for our future aircraft," Mr Eremenko told Bloomberg Television's Yvonne Man in an interview broadcast on Wednesday. "We're pursuing single-pilot operation as a potential option and a lot of the technologies needed to make that happen has also put us on the path towards unpiloted operation." The aerospace industry has begun seeing a similar trend as the car market, where carmakers are investing in or acquiring autonomous driving startups. Plane manufacturers including Airbus and Boeing are racing to develop artificial intelligence that will one day enable computers to fly planes without human beings at the controls.


AI's sharing economy: Microsoft creates publicly available datasets

#artificialintelligence

One big obstacle, they discovered, was that the research area was so new that there weren't any existing datasets available for them to test their hypotheses. The FigureQA dataset, which the team released publicly earlier this fall, is one of a number of datasets, metrics and other tools for testing AI systems that Microsoft researchers and engineers have created and shared in recent years. Researchers all over the world use them to see how well their AI systems do at everything from translating conversational speech to predicting the next word a person may want to type. The teams say these tools provide a codified way for everyone from academic researchers to industry experts to test their systems, compare their work and learn from each other. "It clarifies our goals, and then others in the research community can say, 'OK, I see where you're going,'" said Rangan Majumder, a partner group program manager within Microsoft's Bing division who also leads development of the MS MARCO machine reading comprehension dataset.