Earlier this year, Google CEO Sundar Pichai announced that Google was moving from mobile-first to AI-first. If the company is as successful shifting away from mobile as they were shifting towards mobile, the change could alter more than just Google. It will likely force other companies to change the way they operate in order to keep up. In much the same way that mobile-first required a new approach to strategy, design and development, AI-first will require a new perspective to properly benefit from its impact. Many companies will say they're "AI-first," but how many will truly be able to transform?
In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing "data in motion," because it treats data as a running stream and it learns as the stream flows. Classical offline learning (batch learning) treats data as a static pool, assuming that all data is available at the time of training. Given a dataset, offline learning produces only one final model, with all the data considered simultaneously.
In the previous article, I talked about how one can make use of the internet in a highly productive manner (for those who haven't read the article, can take a look here) which can help increase knowledge and open doors for different opportunities as well. Today, I'll be discussing how startups are making use of artificial intelligence and how they are turning their products and services smarter in order to serve the consumers in a better way. The youth and the coming generations are extremely motivated and inclined towards the idea of starting a startup and pursuing the dream of becoming an entrepreneur. Though this dream comes true only with tons of determination and dedication towards the work you do. Many startups have been made in the recent years and each of them offering a unique product or service is making news in the industry.
It's National Customer Service Week this week, and one of the key themes being discussed is the use of AI technology and chatbots for serving customers. One big debate for the week is whether chatbots will or will not eliminate any inadvertent bias in how customers are treated. A new study from AI experts Pegasystems and issued for National Customer Service Week sets out what British customers think. When questioned about previous human customer service interactions, almost half (49%) of British consumers said they have experienced bias as a result of their individual characteristics, beliefs and/or appearance. By contrast, and despite fears that inherent human bias could be transferred onto modern chatbots, only 8% of respondents feel there is a risk that chatbots will be biased.
The right to due process was inscribed into the US constitution with a pen. A new report from leading researchers in artificial intelligence cautions it is now being undermined by computer code. Public agencies responsible for areas such as criminal justice, health, and welfare increasingly use scoring systems and software to steer or make decisions on life-changing events like granting bail, sentencing, enforcement, and prioritizing services. The report from AI Now, a research institute at NYU that studies the social implications of artificial intelligence, says too many of those systems are opaque to the citizens they hold power over. The AI Now report calls for agencies to refrain from what it calls "black box" systems opaque to outside scrutiny.
Artificial intelligence is all the rage these days. There's broad consensus that AI is the next game-changing technology, poised to impact virtually every aspect of our lives in the coming years, from transportation to medical care to financial services. Gartner predicts that by 2020, AI will be pervasive in almost every new software product and service and the technology will be a top five investment priority for more than 30 percent of CIOs. An area where AI is already showing enormous value is wireless networking. The use of machine learning can transform WLANs into neural networks that simplify operations, expedite troubleshooting and provide unprecedented visibility into the user experience.
Recommender systems are automated computer programs that match items to users in different contexts. Such systems are ubiquitous and have become an integral part of our daily lives. Examples include recommending products to users on a site like Amazon, recommending content to users visiting a website like Yahoo!, recommending movies to users on a site like Netflix, recommending jobs to users on LinkedIn, and so on. Given the significant heterogeneity in user preferences, providing personalized recommendations is key to the success of such systems. To achieve this goal at scale, using machine learning models to estimate user preference from feedback data is essential.
For the past few years, the travel industry has been exploring innovative ways to utilize artificial intelligence (AI), in an effort to unlock the promise of more efficient communications and greater customer service between travelers and service provides. So far, most of that potential has remained largely untapped, despite significant advances in both travel and AI sectors. WayBlazer however, is building an extremely powerful travel recommendation engine, and it's doing it with a little help from AI. WayBlazer's Travel Graph uses artificial intelligence to learn about tens of millions of travel products and thousands of global destinations. It ingests and extracts useful from descriptions, reviews, blogs, images, and videos to develop a frame of travel intelligence that's used to power the most relevant recommendations for today's travelers. By using machine learning models, their travel graph gets smarter with every user search.
The U.K. government has just released a thoughtful report on the potential of AI, along with some jolly sensible recommendations for making the most of the profoundly important technology. The report, coauthored by the Department for Digital, Culture, Media & Sport and the Department for Business, Energy & Industrial Strategy, concludes that "AI offers massive gains in efficiency and performance to most or all industry sectors, from drug discovery to logistics." The technology "can be integrated into existing processes, improving them, scaling them, and reducing their costs, by making or suggesting more accurate decisions through better use of information," it adds. And this could add a whopping $814 billion to the U.K. economy by 2035. To benefit from the AI boom, it says, the U.K. should develop "data trusts" so that data can be shared more easily and securely, and it should make research data more accessible to machines.
The UK economy could benefit from an estimated £630bn provided the government supports efforts to develop and apply Artificial Intelligence in a range of applications including healthcare. These are the conclusions of a report published yesterday, October 15, 2017, which set out proposals for how government can work with industry to take a leading position in AI, a sector singled out for growth in January 2017's Industrial Strategy Green Paper. The independent review, 'Growing the Artificial Intelligence Industry in the UK', was announced as part of the Digital Strategy in March, 2017, and was led by Dame Wendy Hall, Professor of Computer Science at Southampton University, and Jérôme Pesenti, chief executive of BenevolentTech. Pesenti said that the AI review focussed on recommendations that are practicable and deliverable. "By following these recommendations, government, academia and industry can help strengthen the UK's position in the global AI market," he said.