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) …
As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. This will help with making actual design decisions and identifying the right design patterns, including situations when no directly applicable solution exists and you must transfer ideas across domains. In rare cases, machine learning might enable a computer to perform tasks that humans simply can't perform because of speed requirements or the scale of data.
I am spending some cycles on my algorithmic rotoscope work -- which is basically a stationary exercise bicycle for my learning about what is and what is not Machine Learning. I am using it to help me understand and tell stories about Machine Learning by creating images using Machine Learning that I can use in my Machine Learning storytelling. Picture a bunch of Machine Learning gears all working together to help make sense of what I'm doing, and WTF I am talking about? As I'm writing a story on how image style transfer Machine Learning could be put to use by libraries, museums, and collection curators, I'm reminded of what a con machine learning will be in the future, and how it will be a vehicle for the extraction of value and outright theft. My image style transfer work is just one tiny slice of this pie.
The Sensor Acquisition Module, for example, is responsible for communicating and receiving data from sensors, while the Main Module will make predictions and make decisions to send to the engine's control module. This experimental system includes 4 main modules: The Sensor Acquisition Module, the Vision Module, the Occupant-System Communication Module and the Artificial Perception Operation Module. Data from the Sensor Acquisition Module and Vision Module is then transmitted through a communication network to an Artificial Perception Operation Module, which is a powerful computer with an intelligent software capable of predicting the behavior of surrounding objects. Also using object detection based on reflection technique; however, radar sensors use electromagnetic waves to scan objects.
How is predictive data changing the automotive industry and what changes can we expect to see in the future? Connected and autonomous cars are going to benefit most from the inclusion of predictive data because their design centers on data collection and processing. As more and more connected cars hit the roads, data management is going to become an essential tool. Predictive data has already shown potential for preventative maintenance, but this same application could be used to predict software problems and security flaws as well.
The greatest potential for deep learning is in adding business-relevant structure to less-structured, sense-like data -- such as images, audio and other sensor data. Generally when training machine learning algorithms (and deep nets are an extreme example of this), the more data the better. When it comes to the most broadly applicable deep learning problems -- object recognition in images, identification of people and their activities in video, natural language processing -- companies like Google and Facebook already sit atop a tremendous amount of relevant image, video, audio and text data. Thus, I expect artificial intelligence as a service (AIaaS) to be the dominant delivery vehicle for these high-value, broadly applicable use cases for deep learning.
Among all of the self-driving startups working toward Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. While a pedestrian in a camera image is a perceptual pattern, there are also patterns in decision making and motion planning--the right behavior at a four way stop, or when turning right on red, to name two examples--to which deep learning can be applied. This is why many companies working on vehicle autonomy are more comfortable with using traditional robotics approaches for decision making, and restrict deep learning to perception. Reiley agrees: "Your decisions have to be software driven and optimized for deep learning, for software and hardware integration.
Using this morphing tread, the Eagle 360 Urban transforms and adapts to changing road and weather conditions. This new generation of tires will create added value for OEM partners and the evolving providers of Mobility as a Service (MaaS) by maximizing uptime and providing proactive maintenance. Working closely together with Goodyear's designers, the students created Vision UMOD, a vehicle for future cities and adapted to the needs of future mobility. This maximizes uptime and safety, offering an improved mobility user experience at all times for Mobility as a Service (MaaS) providers.
Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. The company's vision systems are a simple, low-cost solution that offers surprisingly sophisticated sensing. This involves capturing images as cars drive around, and annotating them to identify things like road markings, traffic signs, other vehicles, and pedestrians. Stephen Zoepf, executive director of the Center for Automotive Research at Stanford, agrees that Intel's acquisition of Mobileye shows how critical data and machine learning are to the auto industry's future.
Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. By drawing data from multiple sensors in or on machines, machine learning algorithms can "learn" what's typical for the machine and then detect when something abnormal begins to occur. As I discussed in my last post, this huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits.
Among all of the self-driving startups working towards Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. This is why many companies working on vehicle autonomy are more comfortable with using traditional robotics approaches for decision making, and restrict deep learning to perception. "And there are some scenarios where we've improving the algorithm and we need to bootstrap it the right way, so we have a team of human annotators do the first iteration and we iteratively improve the deep learning system. "Your decisions have to be software driven and optimized for deep learning, for software and hardware integration.