Source: Global Web Index
How Companies Are Using IoT Now
Source: ZDNet
Progress in AI and its Future Development
AI has achieved recent performance breakthroughs across numerous cognitive applications (Figure 7), from image classification to pattern recognition and ontological reasoning. This progress is due largely to convergent advances across three enablers: computing power, training data and learning algorithms. To illustrate this, automatedimage recognition and classification has improved in accuracy over the past decade, from 85% to 95% (a human averages 93%), allowing such algorithms to progress from being novelties to enablers of real innovations, such as autonomous transportation for warehouse order picking.Solutions are currently trained on millions of image data, a 100-fold increase compared with a decade ago. They are powered by specialized graphics processing unit chips thatare more than 1,000 times faster, and five to ten times more complex (based on a 150 to 200-layer neural network) than those of previous generations. Computing and storage costs have declined commensurately by an average of 35% year on year.In the near future, AI will build on adoption enablers to unlock faster, smarter and more intuitive applications, although progress will probably be confined to broad adoption of narrow, context-aware intelligence across domains. The chasm separating narrow and general intelligence is believed to represent a fundamentally different set of learning algorithms and non-deterministic computing architecture compared with what exits currently.
Car Pool of the Future: 11 Million Shared Driverless Cars by 2030 and 35 Million Car-Sharing Registered Users by 2021
Fully driverless technology will spark a transformation of personal mobility, enabling consumers to abandon costly vehicle ownership and summon shared vehicles when needed. ABI Research predicts that this will transform the vehicle interior, which car manufacturers will design to be reconfigurable per the individual needs and preferences of whoever is using the vehicle at the time…
ABI Research forecasts that there will be more than 11 million shared driverless vehicles operating on the roads globally by 2030, serving an average of 64 users per shared driverless vehicle.
The size of the urban population and the number of licensed drivers will determine the growth of car sharing in Europe, North America, and Asia-Pacific.
In Europe, some 81 million people will be living in large urban areas in 2021, 46 million of whom will have a valid driver’s license. About 14 million people will be registered with a car-sharing service and 1.4 million of them will be heavy users who take multiple trips per month. The North American urban population is expected to reach 50 million by 2021; 31 million people will be licensed drivers, of whom 6 million will be registered users of a car-sharing service. Some 600,000 people will be heavy users. Asia-Pacific’s urban population will grow to 253 million, and there will be 75 million licensed drivers. Roughly 15 million will be registered with sharing services, and 1.5 million will use them for multiple monthly trips.
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Big Data Landscape 2017: Big Data + AI = New IT Stack
We’re witnessing the emergence of a new stack, where Big Data technologies are used to handle core data engineering challenges, and machine learning is used to extract value from the data (in the form of analytical insights, or actions).
In other words: Big Data provides the pipes, and AI provides the smarts.
Of course, this symbiotic relationship has existed for years, but its implementation was only available to a privileged few.
The democratization of those technologies has now started in earnest. “Big Data + AI” is becoming the default stack upon which many modern applications (whether targeting consumers or enterprise) are being built. Both startups and some Fortune 1000 companies are leveraging this new stack…
Often, but not always, the cloud is the third leg of the stool. This trend is precipitated by all the efforts of the cloud giants, who are now in an open war to provide access to a machine learning cloud.
Will Google Own AI? (4)
We’ve been using compute-intensive machine learning in our products for the past 15 years. We use it so much that we even designed an entirely new class of custom machine learning accelerator, the Tensor Processing Unit. Just how fast is the TPU, actually? Today, in conjunction with a TPU talk for a National Academy of Engineering meeting at the Computer History Museum in Silicon Valley, we’re releasing a study that shares new details on these custom chips, which have been running machine learning applications in our data centers since 2015. This first generation of TPUs targeted inference (the use of an already trained model, as opposed to the training phase of a model, which has somewhat different characteristics), and here are some of the results we’ve seen:
- On our production AI workloads that utilize neural network inference, the TPU is 15x to 30x faster than contemporary GPUs and CPUs.
- The TPU also achieves much better energy efficiency than conventional chips, achieving 30x to 80x improvement in TOPS/Watt measure (tera-operations [trillion or 1012 operations] of computation per Watt of energy consumed).
- The neural networks powering these applications require a surprisingly small amount of code: just 100 to 1500 lines. The code is based on TensorFlow, our popular open-source machine learning framework.
- More than 70 authors contributed to this report. It really does take a village to design, verify, implement and deploy the hardware and software of a system like this.