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) …
Machines are starting to take the place of the people who flip burgers, drive across town and, lately, manage stock portfolios. Artificial intelligence is taking on a bigger role in making investment decisions. A.I., including an ability to analyze data and actually learn from it, is considered useful in executing certain investing models, such as high-frequency trading, and in helping fund managers with tasks that rely on gathering and interpreting reams of information. Going a step further, an exchange-traded fund introduced in October uses A.I. algorithms to choose long-term stock holdings. It is to early to say whether the E.T.F., A.I. Powered Equity, will be a trendsetter or merely a curiosity.
While you've been sleeping, artificial intelligence has been evolving. It isn't something to be afraid of -- yet. In actuality, AI has been present in numerous industries for a long time. As development improves and transforms, both with AI-based analytics, also referred to as deep learning, and user feedback, AI is evolving from being the villain in a bad action movie to helping people live a better life through sleep and health and wellness tracking. As AI innovation improves, so do the apps being implemented in daily life.
The number of Computer Science academic papers and studies has soared by more than 9X since 1996. Academic studies and research are often the precursors to new intellectual property and patents. The entire Scopus database contains over 200,000 (200,237) papers in the field of Computer Science that have been indexed with the key term "Artificial Intelligence." The Scopus database contains almost 5 million (4,868,421) papers in the subject area "Computer Science." There have been a 6X increase in the annual investment levels by venture capital (VC) investors into U.S.-based Ai startups since 2000.
Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision--essentially, to know when they should doubt themselves. Deep learning, which involves feeding example data to a large and powerful neural network, has been an enormous success over the past few years, enabling machines to recognize objects in images or transcribe speech almost perfectly. But it requires lots of training data and computing power, and it can be surprisingly brittle. Somewhat counterintuitively, this self-doubt offers one fix.
Here at The Next Platform, we tend to keep a close eye on how the major hyperscalers evolve their infrastructure to support massive scale and evermore complex workloads. Not so long ago the core services were relatively standard transactions and operations, but with the addition of training and inferencing against complex deep learning models--something that requires a two-handed approach to hardware--the hyperscale hardware stack has had to quicken its step to keep pace with the new performance and efficiency demands of machine learning at scale. While not innovating on the custom hardware side quite the same way as Google, Facebook has shared some notable progress in fine-tuning its own datacenters. From its unique split network backbone, neural network-based viz system, to large-scale upgrades to its server farms and its work honing GPU use, there is plenty to focus on infrastructure-wise. For us, one of the more prescient developments from Facebook is its own server designs which now serve over 2 billion accounts as of the end of 2017, specifically its latest GPU-packed Open Compute based approach.
The onset of the fourth Industrial Revolution, with its technological innovations and advancement, has forced industries and businesses to review and reinvent their processes to avoid becoming obsolete. Although this has caused massive disruption, the resulting opportunities (especially those created by the software and tech industry) are well worth it. One of the more recent developments is the increased use of AI in B2B marketing applications. Although digital marketers have already begun exploring the benefits of machine-learning algorithms, the opportunities for implementing AI in B2B marketing are yet to be exhausted. Some of the areas that still need to be explored include blockchain, predictive analysis, personalization, propensity modeling and lead scoring.
Chatbots may seem basic or rudimentary now, but just wait: AI advancements will take virtual agents to new levels of competency to engage customers. The race is on for software vendors to improve user experiences with chatbots -- a new wave of computer programs that conduct conversations through auditory or text methods. Chatbot development is one frontier confronting artificial intelligence in CRM, as is better lead scoring and automated data entry into CRM records from outside sources, such as email and other back-office systems. Vendors are rushing to improve chatbots before customers tire of their still somewhat unreliable self-service experiences and rudimentary functioning. But even if the companies can't keep up, artificial intelligence technology will.
Even with the support of AI frameworks like TensorFlow or OpenAI, artificial intelligence still requires deep knowledge and understanding compared to a mainstream web developer. If you have built a working prototype, you are probably the smartest guy in the room. Congratulations, you are a member of a very exclusive club. With Kaggle you can even earn decent money by solving real world projects. All in all it is an excellent position to be in, but is it enough to build a business?
While A.I. and data-focused machine learning have been around for decades, the algorithmic technologies have made their presence known in a variety of industries and contexts this year. Microsoft UK's chief envisioning officer Dave Coplin has called A.I. "the most important technology that anybody on the planet is working on today," and Silicon Valley companies seem to have taken that to heart: They've been hiring A.I. experts right and left, and with those in short supply, they've started teaching employees the fundamentals of A.I. themselves. Not every A.I. achievement has been met with admiration and applause, though. Some are worried about the human prejudices that are being introduced into A.I. systems. ProPublica found in 2016, for example, that the software algorithms used to predict future criminals were heavily biased against black defendants.
Artificial Intelligence used to mean something. That app that delivers you late-night egg rolls? The chatbot that pops up when you're buying new kicks? Tweets, stories, posts in your feed, the search results you return, even the people you swipe right or left; artificial intelligence had an invisible hand in what (and who) you see on the internet. But in the walled-off world of health care, with its HIPAA laws and privacy hot buttons, AI is only just beginning to change the way doctors see, diagnose, treat, and monitor patients.