Dear Wheego Enthusiast,
In this issue:
Wheego Electric Cars is now Wheego Technologies
- Wheego Electric Cars is now Wheego Technologies
- Atlanta Helps Steer Development Of Driverless Cars
- Wheego is Hiring
- From Reactive Robots to Sentient Machines: The 4 Types of AI
Electric Cars, Inc. announced this month we have changed our name to Wheego Technologies, Inc.
“The name ‘Wheego Technologies’ better reflects our expanded scope of business,” said CEO Mike McQuary. “Our nine years of experience developing all-electric vehicles will continue to be key to our success, since most of the emerging autonomous vehicles are also electric. Our team now includes industry experts in artificial intelligence, networked communications and autonomous driving technologies. We are actively developing products and services that advance autonomous driving. Our goal is to create a suite of autonomous driving features that are productized and mainstreamed.”
Check out our new webpage at http://www.wheego.net
Atlanta Helps Steer Development Of Driverless Cars
Wheego’s work with Kennesaw State University/Southern Polytechnic State University is mentioned in this WABE/Atlanta Business Chronicle story by Urvaksh Karkaria
With its traffic woes, research universities and corporate offices, metro Atlanta seems uniquely positioned to steer the development of driverless vehicle innovation, according to this week’s Atlanta Business Chronicle
At Georgia Tech, the research is focused on the field of perception — which includes the technologies that allow a vehicle to identify, classify and track objects around it. Researchers are developing software for camera-based 3D mapping. Cameras, radar and lidar systems identify objects, their composition and their speed. 3D mapping lets the car build a model in real time of the world around it, based on the data its sensors and cameras pick up.
Georgia Tech is also working on the public policy side, studying how cities will need to adapt roads, traffic management, parking regulations, even mass transit for a driverless car future.
Georgia Tech has retrofitted a Porsche Cayenne to drive autonomously. With six across town, Kennesaw State University is working with Atlanta-based electric car maker Wheego to develop drive by wire technologies that would, in an emergency, enable customer support to take over control of the car, or access the vehicle’s diagnostic data.
KSU is also studying concepts such as platooning — where autonomous vehicles can travel in tight groups safely and efficiently.
Wheego is Hiring
Visit our “careers”
page to learn more about openings at Wheego.
Current opportunities in Atlanta include:
From Reactive Robots to Sentient Machines: The 4 Types of AI
Excerpted from an article by Arend Hintze in Live Science. Read the full article here.
- C++, Python Programmer
- Video Image Annotation Systems Engineer
The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us?
The new White House report on artificial intelligence
takes an appropriately skeptical view of that dream. It says the next 20 years likely won't see machines "exhibit broadly-applicable intelligence comparable to or exceeding that of humans," though it does go on to say that in the coming years, "machines will reach and exceed human performance on more and more tasks." But its assumptions about how those capabilities will develop missed some important points.
The report focuses on what might be called mainstream AI tools: machine learning and deep learning. These are the sorts of technologies that have been able to play "Jeopardy!" well, and beat human Go masters at the most complicated game ever invented. These current intelligent systems are able to handle huge amounts of data and make complex calculations very quickly. But they lack an element that will be key to building the sentient machines we picture having in the future.
We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.
Type I AI: Reactive machines
The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM's chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.
These methods do improve the ability of AI systems to play specific games better, but they can't be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can't function beyond the specific tasks they're assigned and are easily fooled.
They can't interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver. But it's bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won't ever be bored, or interested, or sad.
Type II AI: Limited memory
This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars' speed and direction. That can't be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.
These observations are added to the self-driving cars' preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They're included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.
But these simple pieces of information about the past are only transient. They aren't saved as part of the car's library of experience it can learn from, the way human drivers compile experience over years behind the wheel.
Type III AI: Theory of mind
We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.
Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called "theory of mind" – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.
Type IV AI: Self-awareness
The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.
This is, in a sense, an extension of the "theory of mind" possessed by Type III artificial intelligences. Consciousness is also called "self-awareness" for a reason. ("I want that item" is a very different statement from "I know I want that item.") Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that's how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.
While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.
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The Wheego Team