For more information on these projects, or if you're interested in joining the group, or enquiring about courses or scholarships, please contact Professor David Powers. Powers, David M. Kaiserslautern FRG. Robot babies: what can they teach us about language acquisition? Leather and J. Sharman, Darryl K. Hardware System Simulation in R. Buyya, Ed. Atyabi, A. Fitzgibbon, S. Powers, D. Journal of Machine Learning Technologies , 2 1 , Lewis, T. Journal of Research and Practice in Information Technology 35 1 , Unsupervised learning of linguistic structure: an empirical evaluation", Int'l Journal of Corpus Linguistics 2 1 : We would be please to supply further information about our activities.
In particular, opportunities exist for high achieving postgraduates to join the program.
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Artificial General Intelligence
Anecdotally, many in AI academia report increasingly meeting very impressive young researchers, including some teenagers, who are incredibly technically proficient and forward thinking in their research, presumably as a result of the democratization of AI tools and education. The other major recent trend has been that fundamental AI research has been increasingly conducted in large Internet companies. Alibaba has Alibaba A. The list goes on. Those industry labs have deep resources and routinely pay millions to secure top researchers. In addition, AI research, particularly in those industry labs, has access to two key resources at unprecedented levels: data and computing power.
The ever-increasing amount of data available to train AI has been well documented by now, and indeed Internet giants like Google and Facebook have a big advantage when it comes to developing broad horizontal AI solutions. There are also rumors of aggregation of data across the various Chinese Internet giants for purposes of AI training.
Beyond data, another big shift that could precipitate AGI is a massive acceleration in computing power , particularly over the last couple of years. To rewind a bit, the team that won the ImageNet competition in the event that triggered much of the current wave of enthusiasm around AI used 2 GPUs to train their network model. This took 5 to 6 days, and was considered the state of the art. But this could be a mere warm-up, as the world is now engaged in a race to produce ever more powerful AI chips and the hardware that surrounds them.
In , Google released the second generation of its Tensor Processing Units TPUs , which are designed specifically to speed up machine learning tasks. Each TPU can deliver teraflops of performance and be used for both inference and training of machine learning models. There is also tremendous activity at the startup level, with heavily-funded emerging hardware players like Cerebras, Graphcore, Wave Computing, Mythic and Lambda, as well as Chinese startups Horizon Robotics, Cambricon and DeePhi.
While still very early from a research standpoint, both Google and IBM announced some meaningful progress in their quantum computing efforts, which would take AI to yet another level of exponential acceleration. The massive increase in computing power opens the door to training the AI with ever increasing amounts of data.
It also enables AI researchers to run experiments much faster, accelerating progress and enabling the creation of new algorithms. The astounding resurrection of AI that effectively started around the ImageNet competition has very much been propelled by deep learning. This statistical technique, pioneered and perfected by several AI researchers including Geoff Hinton, Yann LeCun and Yoshua Bengio, involves multiple layers of processing that gradually refine results see this Nature article for an in depth explanation.
It is an old technique that dates back to the s, s and s, but it suddenly showed its power when fed enough data and computing power. Interestingly, however, as the rest of the world is starting to widely embrace deep learning across a number of consumer and enterprise applications, the AI research world is asking whether it is hitting diminishing returns.
What is AI (artificial intelligence)? - Definition from osunifupob.tk
Geoff Hinton himself at a conference in September questioned back-propagation, the backbone of neural networks which he helped invent, and suggested starting over, which sent shockwaves in the AI research world. There are many variations of unsupervised learning, including autoencoders, deep belief networks and GANs.
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One application is optimizing its industrial processes. Intel is using machine learning to improve sales effectiveness and boost revenue. One approach it takes is automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns.
The resulting categories can be used to prioritize sales efforts and tailor promotions. To improve marketing and customer service, BBVA Compass bank uses a social media sentiment monitoring tool to track and understand what consumers are saying about the bank and its competitors. The tool, which incorporates natural language processing technology, automatically identifies salient topics of consumer chatter and the sentiments surrounding those topics.
Aetna and GNS Healthcare teamed up to use machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them. Their analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke, and diabetes. Using claims and biometric data for a population of 37, Aetna members, the companies developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with the disorder.
As the examples above have shown, cognitive technologies can be used in a variety of ways to create business benefits. Next, we discuss how to sort through potential opportunities. Cognitive technologies are not the solution to every problem. Organizations need to evaluate the business case for investing in this technology in an individualized way.
Our research on how companies are putting cognitive technologies to work has revealed a framework that can help organizations assess their own opportunities for deploying these technologies. We suggest organizations look across their business processes, their products, and their markets to examine where the use of cognitive technologies may be viable, where it could be valuable, and where it may even be vital.
Organizations can use it to screen opportunities for applying cognitive technologies. Cognitive technologies have limits that are not widely acknowledged in the business press. They are not truly intelligent in any general sense of the word; they cannot really see, hear, or understand. No robot can excel at tasks that require empathy, emotion, or relatedness. But there is a broad range of problems for which cognitive technologies can provide at least part of a solution.
The first step in assessing opportunities for the technology is to understand which applications are viable. Some tasks that require human or near-human levels of speech recognition or vision can now be performed automatically or semi-automatically by cognitive technologies. Examples include first-tier telephone customer service, processing handwritten forms, and surveillance. Machine learning techniques are enabling organizations to make predictions based on data sets too big to be understood by human experts and too unstructured to be analyzed by traditional analytics.
And automated reasoning systems can find solutions to problems with incomplete or uncertain information while satisfying complex and changing constraints. They can automate the decision-making process of experts, such as the engineering managers at the subway system in Hong Kong mentioned earlier. Just because something can be automated with cognitive technologies does not mean it is worth doing so.
In other words, what is viable is not necessarily valuable.
Automation features that customers do not care about are obviously not valuable. Tasks performed well by plentiful, low-cost workers are not attractive candidates for automation. Tasks that require scarce expertise may be. These may be good automation candidates. Accountants who scan hundreds of contracts looking for patterns and anomalies in contract terms, for instance, are using their reading skills more than their accounting skills.
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It may be valuable in this scenario to use natural language processing techniques to automate the process of reading and extracting the terms from a body of contracts. For certain business problems, cognitive technologies may be more than just viable and valuable. They may be vital. Processes that require human perception at a very high scale may be unworkable without the support of cognitive technologies.
The Georgia agency mentioned earlier—which has to process 40, campaign finance disclosure forms per month, many of which are handwritten—is an example of this. Another example is Twitter, which uses natural language processing to help advertisers understand when, why, and how its users post comments about television shows and TV advertising; this capability would not be possible without cognitive computing to analyze the language of the tweets.
Especially in large-scale online businesses but increasingly, we expect, in businesses of all types, the performance of certain functions will depend on the use of cognitive technologies. We do not intend to suggest, with our simple three Vs framework, that investing in cognitive technologies is a simple matter. The technologies are still evolving, best practices are scarce, and trial and error may be the way forward, especially for novel applications. The viability of an application may depend on factors such as the specific characteristics of the information an organization is working with.
Value varies with the evolving level of effort required to implement these technologies. And a dynamic competitive landscape may dictate which applications are vital.
With this in mind, we recommend that organizations be systematic when applying the three Vs framework. This means analyzing business processes, staffing models, data assets, and markets to home in on opportunities for applying cognitive technologies. Process maps can highlight tasks that rely more on human perception than special skills, are costly, where scarce expertise might be able to be encoded as rules for use in an automated reasoning system, or where the value of improved performance is high.
Such tasks include reviewing documents, compiling evidence, processing forms, answering basic questions, identifying patterns, planning and scheduling, and diagnosing. Review your staffing model to identify roles where cognitive skills and training may be underutilized or where expertise is in short supply. Sifting through clinical notes in patient records to identify candidates for clinical trials is a task that highly trained nurse practitioners do today.
But much of the job involves reading and comparing keywords. This represents an opportunity for automation with cognitive technologies. Perform a data set inventory to uncover operational data sets that may be under-analyzed and insufficiently exploited. For instance, a jet engine maker is analyzing detailed usage and sensor data from its engines to gain insights about the causes and future timing of maintenance issues. For instance, Nest created a new category—smart thermostats—by recognizing that machine learning could bring new levels of convenience and comfort to home climate control.
Previous research by Deloitte LLP reveals that exceptional companies—those that exhibit superior performance over the long term—tend to differentiate themselves based on value, not price. And they seek to grow revenues before cutting costs. As noted above, Associated Press is taking this approach by using automation and is opting to increase its output of earnings stories rather than maintain its previous level at a lower cost.
Despite the impressive capabilities of cognitive technologies, nothing we have seen suggests that a wholesale replacement of human workers by robotic substitutes is imminent. Computer vision has made great strides in recent years—Facebook claims it can recognize faces with 97 percent accuracy—but it still cannot generally recognize multiple objects in a scene or reliably understand what actions it is witnessing.
Systems that use natural language processing can dramatically accelerate the process of analyzing and understanding documents.
But they make simple mistakes that an average human would not. And we need humans to act on the insights that may be gleaned by automatic document analysis. Not only may cognitive systems produce imperfect results, they may also require a significant investment of human time to train or configure before they can do their work. Machine learning systems are routinely exposed to thousands or millions of data elements before they can start reliably making predictions or classifications.
Indeed, a promising approach for making effective use of cognitive systems is designing them to work hand-in-hand with people, leveraging the strength of each.