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) …
BENGALURU: Given today's fast-changing world, as high as 62% of Indian professionals said in a survey by LinkedIn that they felt daunted by the rapid pace of change in skills that are in demand. A high proportion (45%) of those surveyed left organisations because of lack of learning and development opportunities, according to LinkedIn, which surveyed about 5,000 employees and professionals in Australia, India, Japan, and Singapore. And this mismatch between market requirements, labour skills and opportunities is expected to grow more acute in future. By 2020, it is estimated that Asia-Pacific (Apac) will face a labour shortage of 12.3 million workers at an annual opportunity cost of $4.2 trillion. By 2020, it is also expected that 42% of the core skills required for a job will change, LinkedIn said.
Everyone is talking about the power of AI and it's slowly invading our lives--emphasis on slowly. While you might have a few AI assistants and connected devices in your house, the business world hasn't fully jumped on board yet with AI. Sure, the forward-thinking companies have and those are the headline we are seeing, but I'm talking about full adoption from the mom and pop shops all the way up to the enterprise level, spanning across all industries. We all understand the power and the potential of AI, but we don't seem to discuss the AI deployment challenges that many businesses are likely facing. We see statistics like 61 percent of companies with an innovation strategy are using AI to identify opportunities in data and think that a majority of companies must be adopting AI.
Ride-hailing company Lyft has passed an important mile-marker in its self-driving car program by successfully delivering more than 50,000 rides, making it the most well-trafficked U.S. program of its kind. According to the company, the milestone marks a ten-fold increase over its total in August 2018 when the program announced that it reached 5,000 rides. The program currently operates 30 modified BMW's within a 20 square-mile area in Las Vegas where it delivers riders to some of the city's most heavily trodden areas, like the Las Vegas strip. Lyft has become the the largest ride-hailing program that lets users take self-driven vehicles. Lyft says the precedent is significant not just for the sheer volume of rides, but for the reported quality.
Buzz about artificial intelligence has led to increased spending and put several Trump administration directives in motion, but only a handful of agencies have gotten into the early stages of AI adoption. However, a second wave of agencies may soon launch their own AI tools if they can overcome some common hurdles. The Professional Services Council Foundation, in a report released Wednesday, highlighted some of the challenges and opportunities agencies face in using AI to deliver on their mission. Looking across four agencies -- Defense Department, the General Services Administration, NASA and the Department of Health and Human Services -- the report highlights use cases where program offices have pioneered AI to reduce backlogs or increase the output of their existing workforce. "They've turned to AI to say, 'Are there routine decisions that we make on a regular basis that AI is now competent enough to handle in a way that we can delegate those decision processes to?'" Dominic Delmolino, the chief technology officer at Accenture Federal Services, said Wednesday at a briefing with reporters.
As Lyft and Uber are on the verge of going public Elon Musk has announced that Tesla too plans to join the lift-sharing sector-albeit in a slightly different way. The news was announced via a response to a twitter post from @LivingTesla who complained about the camera on the rearview mirror of the car. The Tesla fan stated that until they know its purpose they would cover the camera. Musk responded saying the camera was installed there to monitor the interior of the car during a rideshare type experience once the car becomes apart of the "Tesla shared autonomy fleet." It's there for when we start competing with Uber/Lyft & people allow their car to earn money for them as part of the Tesla shared autonomy fleet.
Please join us at our home location of BBC Broadcast Centre, White City Place, for the March edition of our Machine Learning Fireside Chats. Let's gather around the 4K TV fireplace for a bite to eat and a beverage, and tune into a panel discussion of industry practitioners and experts. "AI is being used in myriad ways to enable social good but how does the [hopefully the government as well] charity sector - not traditionally at the sharp end of technology - ensure it doesn't go awry, especially when charitable organisations serve some of the most vulnerable people, and public trust and goodwill are particularly important to their core mission. How easy is it, really, to achieve social good with AI? Is the data available to support the effort?
According to a survey conducted by Dun & Bradstreet among the AI World Conference and Expo attendees in Boston, a lack of internal human expertise, as well as a lack of data, are the two greatest hurdles to implementing AI across organizations in 2019 (tied at 28 percent). Other challenges anticipated for 2019 are technology infrastructure (17 percent), hesitation from C-suite/executive decision makers and lack of budget (tied at eight percent), regulatory challenges (seven percent) and the lack of a strong digital base (one percent). "Data is the foundation upon which any technology – especially AI – can be built. If you have a faulty data foundation, you will likely have a faulty technology approach yielding faulty insights. As data continues to be produced and stored in exponentially increasing quantities, we will begin to see AI systems adapt and improve, which is inherent to the value of AI," said Anthony Scriffignano, Ph.D., a chief data scientist at Dun & Bradstreet.
Recent works have highlighted the strengths of the Transformer architecture for dealing with sequence tasks. At the same time, neural architecture search has advanced to the point where it can outperform human-designed models. The goal of this work is to use architecture search to find a better Transformer architecture. We first construct a large search space inspired by the recent advances in feed-forward sequential models and then run evolutionary architecture search, seeding our initial population with the Transformer. To effectively run this search on the computationally expensive WMT 2014 English-German translation task, we develop the progressive dynamic hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments - the Evolved Transformer - demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At big model size, the Evolved Transformer is twice as efficient as the Transformer in FLOPS without loss in quality. At a much smaller - mobile-friendly - model size of ~7M parameters, the Evolved Transformer outperforms the Transformer by 0.7 BLEU on WMT'14 English-German.
The excitement surrounding artificial intelligence (AI) today reflects not only how AI applications could transform businesses and economies, but also the hope that they can address challenges such as cancer and climate change. The idea that AI could revolutionize people's well-being is obviously appealing. But just how realistic is it? To answer that question, we (at McKinsey Global Institute) examined more than 150 scenarios in which AI is being applied or could be applied for social good. What we found is that AI could make a powerful contribution to resolving many types of societal challenges, but it is not a silver bullet.
Successful industrialization of driverless cars will depend on getting over many significant hurdles. Failure only requires getting tripped up by a few of them. In part two of this series, I outlined seven key hurdles to industrial-size scaling of driverless cars. Overcoming hurdles to scaling is not enough, however. In this concluding article, I explore the challenges to broader market acceptance.