If manufacturers want to sustain and grow their customer bases in a competitive environment, their products need to fulfill increasingly high quality and reliability standards. Automakers, for example, now have a target defect rate for the integrated systems of less than 1 percent. That's putting pressure on the original equipment makers (OEMs) and their suppliers who have to meet these targets at the same time that products and manufacturing processes are becoming increasingly complex and featuring numerous activities that impact quality, performance, and yield. To prevent failures of components, systems, and ultimately the product, these manufacturers need reliable methods to find defects. But quality control today is, in many cases, still performed by human inspectors, which limits its reliability and efficiency.

Deals to AI startups reached a record high last year, from 160 deals in 2012 to 658 in 2016. Although non-US deal share has been increasing over the time period, well over a majority of deals, around 70%, went to startups in the US in the last 5 years. The top 3 states for deals were California (51%), New York (11%), and Massachusetts (9%). Using the CB Insights database, we mapped the most well-funded AI startups in each of the 35 states where there have been equity deals to an AI company in the last five years. The map only includes companies that have not exited and only includes equity financing.

Artificial intelligence is one of the most hot-button topics these days. Countless industries are beginning to utilize AI tools in the interest of becoming more agile and responding to market demands more quickly and efficiently – and the finance realm is no different. In fact, AI is becoming a major buzzword in the financial services industry. Banks are beginning to utilize these kinds of technologies to make sense of all the customer data flowing into their organizations, which helps to capitalize on key insights and create better business practices by addressing industry concerns as they come up. It's becoming clear that banks will need to create even stronger AI strategies in the near future in order to deal with the rapidly changing regulatory environment and customer demand.

What if artificial intelligence actually made a difference in our everyday lives? If you think about it, the technology for processing information more like a human is still in an early stage. It shows up in chatbots and on speakers like the Amazon Echo. Yet, many of the services we use each day are still not AI-enabled, which is unfortunate. It's one thing to make a car we can't afford or a speaker smarter, but how about these common products and services?

When Everest Group asked me to lead its research on Service Delivery Automation (SDA) last year, the significance of this new role was not immediately clear to me. It is only now, a year older and wiser, that I realize that new roles and job titles are harbingers of change. They are one of the many effects of disruptive technology and its impact on the workplace. What are the implications and why is the emergence of such new titles significant? New titles are popping up all over the industry.

It's no secret that technology is revolutionising consumer and brand interaction. In most cases, consumers are changing their behaviour faster than most retailers can adapt their marketing strategies. Marketers must engage savvy shoppers across a plethora of channels, the competition is intense, and customer satisfaction and retention have become top priorities for most brands. In our modern era, it is imperative to know and UNDERSTAND who our customers are, what they like/dislike, what will motivate them to buy or buy again, and why they leave. It is vital to have a forward-looking approach and to predict answers to questions such as: "What will my customer be interested in next week? In which city is my customer likely to shop? What is the most effective channel to connect with customers when they are ready to buy? Which products are my prospects waiting for?"

Thus, given a sequence of frames corresponding to an utterance, the model is required to produce, for each frame, a probability distribution over the alphabet. During the training phase, the softmax outputs are fed into a CTC cost function (more on this shortly) which uses the actual transcripts to (i) score the model's predictions, and (ii) generate an error signal quantifying the accuracy of the model's predictions. The overall goal is to train the model to increase the overall score of its predictions relative to the actual transcripts. Empirically, we have found that using stochastic gradient descent with momentum paired with gradient clipping leads to the best performing models. Deeper networks (seven layers or more) also tend to perform better in general.

Public sector agencies must adopt emerging technologies – including machine learning, artificial intelligence, and biometrics – to attract and retain more technically adept employees, a new report from Accenture recommends. It said this is critical to addressing a widening skills gap and strong competition from a better financed private sector. According to the report, "Emerging Technologies in Public Service," the need to attract technically proficient employees is becoming even more urgent as the existing workforce continues to age, creating an irrevocable loss of institutional knowledge unless action is taken now. The report emphasized that hiring and developing people with the necessary skills, including the need for emerging technology specialists, is one of the top three challenges across all industries and countries today," the report noted. "The very concept of work is being redefined as different generations enter and exit the workforce in a rapidly changing technological landscape," said Terry Hemken, who leads Accenture's Health & Public Service Analytics Insights for Government business. "Government leaders must make every effort to reskill their people to be relevant in the future and ready to adapt to change." Survey respondents said emerging technologies will augment existing roles rather than replace them. Automating tasks, whether through artificial intelligence, machine learning or other technologies, frees up employees to focus on activities that are more critical and more closely aligned with citizen needs, according to the research. In fact, eight in 10 respondents said that implementing emerging technologies will improve job satisfaction and can aid staff retention, partly by automating certain repetitive tasks and making others more aligned with citizens' direct needs. Nearly 60 percent of respondents also said that being able to implement projects using emerging technologies would require significant investment in reskilling existing staff. "Responsive and responsible leaders must ensure that their people are relevant and adaptable to keep pace with technology," Hemken said. "Creating the future workforce now is the responsibility of the very highest levels of an organization.

Seiya Tokui is a researcher at Preferred Networks, Inc. and a Ph.D. student at the University of Tokyo, from which he received the master's degree in mathematical informatics in 2012. He is the lead developer of Chainer, a deep learning framework. His research interests include deep learning and generative models.

Pundits are quick to hype AI and machine learning as the future of everything. But, anyone who has been caught screaming at Siri for its lack of understanding of the most basic of queries knows that we have a long, ponderous way to go before "we have arrived." That's why I find Gil Press' summary of the recent O'Reilly AI Conference so helpful and important. Some of the observations are banal ("AI is not going to exterminate us, AI is going to empower us"), but others capture the essence of what makes AI so promising...and beguiling. The first observation ("AI is difficult") seems obvious, yet for all the wrong reasons.