What exactly is AI and how does it work? Read on to learn everything you need to know about AI.
Artificial intelligence (AI) is incorporated into all products and services. From personalized recommendations on music and video streaming services to the incoming generation of connected cameras and autonomous cars, AI is changing our lives in ways big and small. Some are even worried that AI can become sentient and we might have a real-life Rise of the Ultron happening amongst us soon. Elon Musk even likens Twitter to “cybernetic super-intelligence”.
But what exactly is Artificial Intelligence and how does it work?
We know that it’s complicated. You have questions and we have answers, so we’re here to break them down.
Artificial intelligence, or AI, is an umbrella term representing a range of techniques that allow machines to mimic human intelligence.
When humans think, they sense what’s happening in their environment, realize what those inputs mean, make a decision based on them, and then act. Artificially intelligent devices are in the early stages of beginning to employ similar judgments and replicate these same behaviors. This may include extreme cases where AI is used in military spaces, or simpler applications like the voice assistants in our phones.
Machine learning is a subset of AI that refers to a machine’s ability to think without being externally programmed every time it needs to learn something new. Traditional devices are programmed with a set of rules for how to act with the help of if-then-else statements. But machine learning enables devices to continuously think about how to act based on data they intake.
Can you create an artificially intelligent piece of software or machine primarily through if-then-else statements? Yes! A simple one but, yes. Once the machine learns how to respond to various inputs, it will be considered intelligent enough to deal with the situations it was created to deal with.
An AI assistant is a program that can respond to you, provide information, and perform tasks at your request. While these assistants are most commonly thought of in terms of smartphones and smart home speakers, they can exist in a range of devices and will become common in XR glasses, home appliances, connected cars, and more. With the 4th generation Qualcomm AI Engine, we are supporting such use cases at power consumption levels that work for mobile devices. Google Assistant, Siri, Cortana, and Alexa are some of the most common AI-driven virtual assistants.
AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants, such as Amazon’s Alexa and Apple’s Siri, to recognize who and what is in a photo, spot spam, or spot spam detect credit card fraud.
At a very high level, artificial intelligence can be split into two broad types: Narrow AI and General AI.
Narrow AI is what we see all around us in computers today – intelligent systems that have been taught or have learned how to carry out specific tasks without being explicitly programmed how to do so.
This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, or in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do defined tasks, which is why they are called narrow AI.
General AI is very different and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets or reasoning about a wide variety of topics based on its accumulated experience.
This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn’t exist today – and AI experts are fiercely divided over how soon it will become a reality.
There are a vast number of emerging applications for narrow AI:
A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Müller and philosopher Nick Bostrom reported a 50% chance that Artificial General Intelligence (AGI) would be developed between 2040 and 2050, rising to 90% by 2075. The group went even further, predicting that so-called ‘superintelligence’ – which Bostrom defines as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” – was expected some 30 years after the achievement of AGI.
While modern narrow AI may be limited to performing specific tasks, within their specialisms, these systems are sometimes capable of superhuman performance, in some instances even demonstrating superior creativity, a trait often held up as intrinsically human.
There have been too many breakthroughs to put together a definitive list, but some highlights include:
In 2009 Google showed its self-driving Toyota Prius could complete more than 10 journeys of 100 miles each, setting society on a path towards driverless vehicles.
In 2011, the computer system IBM Watson made headlines worldwide when it won the US quiz show Jeopardy!, beating two of the best players the show had ever produced. To win the show, Watson used natural language processing and analytics on vast repositories of data that is processed to answer human-posed questions, often in a fraction of a second.
In 2012, another breakthrough heralded AI’s potential to tackle a multitude of new tasks previously thought of as too complex for any machine. That year, the AlexNet system decisively triumphed in the ImageNet Large Scale Visual Recognition Challenge. AlexNet’s accuracy was such that it halved the error rate compared to rival systems in the image-recognition contest.
Another area of AI research is evolutionary computation.
It borrows from Darwin’s theory of natural selection. It sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem.
This approach has even been used to help design AI models, effectively using AI to help build AI. This use of evolutionary algorithms to optimize neural networks is called neuroevolution. It could have an important role to play in helping design efficient AI as the use of intelligent systems becomes more prevalent, particularly as demand for data scientists often outstrips supply. The technique was showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.
Finally, there are expert systems, where computers are programmed with rules that allow them to take a series of decisions based on a large number of inputs, allowing that machine to mimic the behavior of a human expert in a specific domain. An example of these knowledge-based systems might be, for example, an autopilot system flying a plane
All of the major cloud platforms – Amazon Web Services, Microsoft Azure and Google Cloud Platform – provide access to GPU arrays for training and running machine-learning models, with Google also gearing up to let users use its Tensor Processing Units – custom chips whose design is optimized for training and running machine-learning models.
Internally, each tech giant and others such as Facebook use AI to help drive myriad public services: serving search results, offering recommendations, recognizing people and things in photos, on-demand translation, spotting spam – the list is extensive.
But one of the most visible manifestations of this AI war has been the rise of virtual assistants, such as Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana.
Relying heavily on voice recognition and natural-language processing and needing an immense corpus to draw upon to answer queries, a huge amount of tech goes into developing these assistants.
But while Apple’s Siri may have come to prominence first, it is Google and Amazon whose assistants have since overtaken Apple in the AI space – Google Assistant with its ability to answer a wide range of queries and Amazon’s Alexa with the massive number of ‘Skills’ that third-party devs have created to add to its capabilities.
Over time, these assistants are gaining abilities that make them more responsive and better able to handle the types of questions people ask in regular conversations. For example, Google Assistant now offers a feature called Continued Conversation, where a user can ask follow-up questions to their initial query, such as ‘What’s the weather like today?’, followed by ‘What about tomorrow?’ and the system understands the follow-up question also relates to the weather.
These assistants and associated services can also handle far more than just speech, with the latest incarnation of the Google Lens able to translate text into images and allow you to search for clothes or furniture using photos.
Despite being built into Windows 10, Cortana has had a particularly rough time of late, with Amazon’s Alexa now available for free on Windows 10 PCs. At the same time, Microsoft revamped Cortana’s role in the operating system to focus more on productivity tasks, such as managing the user’s schedule, rather than more consumer-focused features found in other assistants, such as playing music.
It’d be a big mistake to think the US tech giants have the field of AI sewn up. Chinese firms Alibaba, Baidu, and Lenovo, invest heavily in AI in fields ranging from e-commerce to autonomous driving. As a country, China is pursuing a three-step plan to turn AI into a core industry for the country, one that will be worth 150 billion yuan ($22bn) by the end of 2020 to become the world’s leading AI power by 2030.
Baidu has invested in developing self-driving cars, powered by its deep-learning algorithm, Baidu AutoBrain. After several years of tests, with its Apollo self-driving car having racked up more than three million miles of driving in tests, it carried over 100 000 passengers in 27 cities worldwide.
Baidu launched a fleet of 40 Apollo Go Robotaxis in Beijing this year. The company’s founder has predicted that self-driving vehicles will be common in China’s cities within five years.
The combination of weak privacy laws, huge investment, concerted data-gathering, and big data analytics by major firms like Baidu, Alibaba, and Tencent, means that some analysts believe China will have an advantage over the US when it comes to future AI research, with one analyst describing the chances of China taking the lead over the US as 500 to 1 in China’s favor.
While you could buy a moderately powerful Nvidia GPU for your PC – somewhere around the Nvidia GeForce RTX 2060 or faster – and start training a machine-learning model, probably the easiest way to experiment with AI-related services is via the cloud.
All of the major tech firms offer various AI services, from the infrastructure to build and train your own machine-learning models through to web services that allow you to access AI-powered tools such as speech, language, vision and sentiment recognition on-demand.
There are many platforms available that can help you hire an AI developer. With the internet so fast and accessible, you can hire a dev with a single click. If you are looking to hire an AI developer, set up a quick appointment with us and check out our marketplace of engineers and developers for hire.
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