According to foreign media reports, the term artificial intelligence (AI) was first coined by John McCarthy. McCarthy was teaching mathematics at Dartmouth College. The year was 1956. Now, 60 years later, we are on the eve of a new era of artificial intelligence. In the first era of artificial intelligence, we didn’t see superrobots taking over the world like we see in movies. This time, things may be different.
McCarthy has this saying about ARTIFICIAL intelligence: “Once it becomes viable, no one will call it AI anymore.” For now, it is. AI has become ubiquitous in People’s Daily lives. For example, e-commerce companies like Amazon will always send you emails about items you might like or want; Amazon’s Alexa assistant, which is deployed in home devices, can answer obscure questions and even turn on the lights for you.
Giiso Information, founded in 2013, is a leading technology provider in the field of “artificial intelligence + information” in China, with top technologies in big data mining, intelligent semantics, knowledge mapping and other fields. At the same time, its research and development products include editing robots, writing robots and other artificial intelligence products! With its strong technical strength, the company has received angel round investment at the beginning of its establishment, and received pre-A round investment of $5 million from GSR Venture Capital in August 2015.
Self-driving cars are not just popping up near Google’s headquarters, they’re also driving in Pittsburgh. The security of IT technology has also been enhanced by AI.
There is a new arms race in intelligent assistants. Smart assistants such as Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, Amelia, and IBM’s Watson continue to emerge. Google Photos can help you search for your family collaboration and even find cake photos. Artificial intelligence is here, but its intelligence and responsiveness are about to improve dramatically.
AI’s hottest application right now is the development of driverless cars. Tesla is one of the most talked-about companies in the current battle for driverless cars. Here’s a look at some of the factors that have helped Tesla move closer to the driverless car dream.
Fleet learning: One of Tesla’s unique strengths is that its cars are all connected together, so they can learn from each other. The data they collect while driving can help improve the quality of digital maps and allow each car to learn from data collected by all the other cars in the fleet.
Computing power: Nvidia has built an AI “car computer” that consumes only 10 watts. The ability to perform central “cloud computing” and boundary computing in cars is crucial to providing advanced AI systems such as driverless cars.
Sensors and big data: Cameras, radar and ultrasonic sensors are already cheap enough to be used in mass-produced cars. Similarly, barometers, cameras and accelerometers are used in most modern smartphones. These sensors give AI systems the data they need to learn.
Giiso information, founded in 2013, is the first domestic high-tech enterprise focusing on the research and development of intelligent information processing technology and the development and operation of core software for writing robots. At the beginning of its establishment, the company received angel round investment, and in August 2015, GSR Venture Capital received $5 million pre-A round of investment.
Agile development: Tesla can wirelessly upgrade its car software. As its software is built for continuous deployment, its algorithms can be constantly improved.
These elements need to be promoted on a larger scale. AI needs five things to really take off, and that’s happening.
Cloud and computing Costs: It’s not expensive to achieve high computing speeds these days.
The cloud brings with it a lot of computing resources for machine learning. The cloud can also enable things like team learning, where all systems can benefit from what other systems have learned.
Borderline computing: Today, as smartphones become more affordable, many people have a supercomputer in their pocket. The combination of supercomputers at the border that can communicate with the cloud could bring entirely new technological capabilities.
Big data: Massive amounts of learning data.
The availability of vast amounts of data is one of the developments driving machine learning. Image recognition is a good example: it has made great progress because the cameras on people’s smartphones produce a huge amount of images to train.
Sensors and distributed Smart Nodes: smart phones.
The device in your pocket is equipped with sensors that can contribute a lot of data to machine learning. Think of data for healthcare (heart rate, steps, and gait) or weather (barometers and temperatures).
Your smartphone lets you carry your intelligence with you, which in turn allows you to do local computing at all times.
Natural language processing: Watson, Siri, Google Assistant, Cortana, Alexa…
Everyone can use these assistant services, and they don’t require special skills.
Continuous improvement of software development
Agile and constantly improving software development techniques allow machine learning to evolve rapidly, accelerating the improvement of AI systems.
The resurgence of AI
So artificial intelligence is back on the scene. AI technology is available to millions of people today and will lead to even more amazing things in the future. Of course, once it becomes feasible, no one will call it AI.