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Artificial Intelligence is shaping the future of humanity across nearly every industry. It is already the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. IFM is merely one of several AI pioneers in a sector that is constantly expanding. For instance, 2,300 of the 9,130 patents granted to IBM inventors in 2021 were with artificial intelligence. Elon Musk, the founder of Tesla and a giant of the IT industry, contributed $10 million to support ongoing research at the nonprofit research organization Open AI. However, compared to his $1 billion co-pledge from 2015, this donation is a mere drop in the bucket. After an evolutionary phase that started with "knowledge engineering" and lasted over several decades marked by periodic dormancy, technology advanced to model- and algorithm-based machine learning and increasingly centered on perception, reasoning, and generalization. Now AI has re-taken center stage like never before, and it won’t cede the spotlight anytime soon.  AI is important because it forms the very foundation of computer learning. Through AI, computers have the ability to harness massive amounts of data and use their learned intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans.

 

Industries to Be Changed by AI

There’s virtually no major industry modern AI,  more specifically, “narrow AI,” which performs objective functions using data-trained models and often falls into the categories of deep learning or machine learning hasn’t already been affected. That’s especially true in the past few years, as data collection and analysis have ramped up considerably thanks to robust IoT connectivity, the proliferation of connected devices, and ever-speedier computer processing.

Some sectors are at the start of their AI journey, others are veteran travelers. Both have a long way to go. Regardless, the impact AI is having on our present-day lives is hard to ignore.

Transportation: Although it could take some time to perfect them, autonomous cars will one day ferry us from place to place.

Manufacturing: AI-powered robots work alongside humans to perform a limited range of tasks like assembly and stacking, and predictive analysis sensors keep equipment running smoothly.

Healthcare: In the comparatively AI-nascent field of healthcare, diseases are more quickly and accurately diagnosed, drug discovery is sped up and streamlined, virtual nursing assistants monitor patients and big data analysis helps to create a more personalized patient experience.

Education: Textbooks are digitized with the help of AI, early-stage virtual tutors assist human instructors and facial analysis gauges the emotions of students to help determine who’s struggling or bored and better tailor the experience to their individual needs.

Media: Journalism is harnessing AI, too, and will continue to benefit from it. Bloomberg uses Cyborg technology to help make quick sense of complex financial reports. The Associated Press employs the natural language abilities of Automated Insights to produce 3,700 earning-reports stories per year — nearly four times more than in the recent past.

Customer Service: Last but hardly least, Google is working on an AI assistant that can place human-like calls to make appointments at, say, your neighborhood hair salon. In addition to words, the system understands context and nuance.

AI Impacts on Future Society

In the warehouses of online giant and AI powerhouse Amazon, which buzz with more than 100,000 robots, picking and packing functions are still performed by humans but that will change. Today’s AI is useless in two significant ways: it has no creativity and no capacity for compassion or love. Rather, it is a tool to amplify human creativity. Those with jobs that involve repetitive or routine tasks must learn new skills so as not to be left by the wayside. Amazon even offers its employees money to train for jobs at other companies. One of the absolute prerequisites for AI to be successful in many areas is that we invest tremendously in education to retrain people for new jobs. People need to learn about programming like they learn a new language he says, “and they need to do that as early as possible because it really is the future. In the future, if you don’t know to code, you don’t know to program, and it’s only going to get more difficult. And while many of those who are forced out of jobs by technology will find new ones, Vandegrift says, that won’t happen overnight. As with America’s transition from an agricultural to an industrial economy during the Industrial Revolution, which played a big role in causing the Great Depression, people eventually got back on their feet. The short-term impact, however, was massive.

AI in the Near Future

In Mendelson’s view, some of the most intriguing AI research and experimentation that will have near-future ramifications is happening in two areas: “reinforcement” learning, which deals in rewards and punishment rather than labeled data; and generative adversarial networks (GAN for short) that allow computer algorithms to create rather than merely assess by pitting two nets against each other. The former is exemplified by the Go-playing prowess of Google DeepMind’s Alpha Go Zero, the latter by original image or audio generation that’s based on learning about a certain subject like celebrities or a particular type of music. On a far grander scale, AI is poised to have a major effect on sustainability, climate change, and environmental issues. Ideally and partly through the use of sophisticated sensors, cities will become less congested, less polluted, and generally more livable. AI is projected to have a lasting impact on just about every industry imaginable as 60 percent of businesses are predicted to be affected by it. We’re already seeing artificial intelligence in our smart devices, cars, healthcare system, and favorite apps, and we’ll continue to see its influence permeate deeper into many other industries for the foreseeable future.

 

Possibilities of Artificial General Intelligence

Speaking at London’s Westminster Abbey in late 2018, internationally renowned AI expert Stuart Russell joked (or not) about his “formal agreement with journalists that I won’t talk to them unless they agree not to put a Terminator robot in the article.” His quip revealed an obvious contempt for Hollywood representations of far-future AI, which tend toward the overwrought and apocalyptic. What Russell referred to as “human-level AI,” also known as artificial general intelligence, has long been fodder for fantasy. But the chances of its being realized anytime soon, or at all, are pretty slim. Russel also pointed out that AI is not currently equipped to fully understand language. This shows a distinct difference between humans and AI at the present moment: Humans can translate machine language and understand it but AI can’t do the same for human language. However, if we reach a point where AI is able to understand our languages, AI systems would be able to read and understand everything ever written. 

“Once we have that capability, you could then query all of the human knowledge and it would be able to synthesize and integrate and answer questions that no human being has ever been able to answer,” Russell added, “because they haven’t read and been able to put together and join the dots between things that have remained separate throughout history.” This offers us a lot to think about. On the subject of this, emulating the human brain is exceedingly difficult and yet another reason for AGI’s still-hypothetical future. Longtime University of Michigan engineering and computer science professor John Laird has conducted research in the field for several decades. “The goal has always been to try to build what we call the cognitive architecture, what we think is innate to an intelligence system,” he says of work that’s largely inspired by human psychology. “One of the things we know, for example, is the human brain is not really just a homogenous set of neurons. There’s a real structure in terms of different components, some of which are associated with knowledge about how to do things in the world.”

That’s called procedural memory. Then there’s knowledge based on general facts, a.k.a. semantic memory, as well as knowledge about previous experiences (or personal facts) which is called episodic memory. One of the projects at Laird’s lab involves using natural language instructions to teach a robot simple games like Tic-Tac-Toe and puzzles. Those instructions typically involve a description of the goal, a rundown of legal moves, and failure situations. The robot internalizes those directives and uses them to plan its actions. As ever, though, breakthroughs are slow to come slower, anyway, than Laird and his fellow researchers would like.

 

 

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