ORLANDO, Fla. – Speech recognition technologies have improved so much in recent years – thanks to cloud computing and advances in machine learning – that the virtual assistants created by Amazon, Google and Apple have quickly become popular with consumers. So it should come as little surprise that the underlying natural language technology is making inroads at work, too. "I would say that it [enterprise adoption] is in early stages now, but there are certainly basic capabilities here today," Jon Arnold, of J Arnold & Associates, said at the Enterprise Connect conference last week. The main uses for speech recognition in the office will, at least at first, revolve around improving employee productivity and automating workflows. Thanks to advances in artificial intelligence (A.I.) techniques, the accuracy of speech recognition systems has improved significantly, with Google and others passing the 95% accuracy mark.
Where some businesses are employing artificial intelligence to sell you more, IBM is using it to sell you less. Specifically, it's employing one set of AI tools to minimize the amount of compute time on its cloud services you need to buy in order to train another set of AI tools to run your business. That will also allow IBM's customers to make the most of another scarce and expensive resource, AI expertise, according to Ruchir Puri, Chief Architect for IBM Watson and an IBM Fellow. "We're lowering the barrier to entry for machine learning capabilities for enterprise," Puri said. The barrier Puri is talking of is the scarcity of human expertise in deep learning, a way of training an artificial intelligence in a particular domain of expertise.
In fields like accountancy and medicine, artificial intelligence is seen as a great savior to humanity -with regard to handling repetitive and seemingly complex tasks. But to security and a few other areas of applications, experts think it would cause havoc in case of misuse. Accountancy firms to be particular are busy investing in AI and automation initiatives to help staffs with mundane tasks. The need is so open in some situations that businesses fail to deliver their mandate to customers. Repetitive tasks have been found to consume more than 60 percent of accountant's time, and now reports show that half of the accountants wish to adopt or have already engaged AI and automation techs to assist, as client demand remains steady.
Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control of computing systems with the help of sandbox experiments, and (2) prediction of the cost of "spot" resources for decision making of cloud clients. In this paper, we formalize debugging by counterfactual probabilities and control by post-(soft-)interventional probabilities. We prove that counterfactuals can approximately be calculated from a "stochastic" graphical causal model (while they are originally defined only for "deterministic" functional causal models), and based on this sketch an approach to address problem (1). To address problem (2), we formalize bidding by post-(soft-)interventional probabilities and present a simple mathematical result on approximate integration of "incomplete" conditional probability distributions. We show how this can be used by cloud clients to trade off privacy against predictability of the outcome of their bidding actions in a toy scenario. We report experiments on simulated and real data.
Gaining an advantage in the global high-tech market is often the result of research and development (R&D) combined with execution and incremental improvement. Today's emerging cloud-based artificial intelligence (AI) platforms are a perfect example of R&D-fueled competition. With the cloud giants and many governments investing heavily in AI R&D, what's a mere multinational corporation to do?
Gaining an advantage in the Global high-tech market is often the result of research and development (R&D) combined with execution and incremental improvement. Today's emerging cloud-based artificial intelligence (AI) platforms are a perfect example of R&D-fueled competition. With the cloud giants and many governments investing heavily in AI R&D, what's a mere multinational corporation to do?
Cloud computing is already a huge business, and competition is stiff. But this year, tech firms opened a new front in the battle to win users over in the cloud: the large-scale introduction of cloud-based AI. For small and medium-size companies, building AI-capable systems at scale can be prohibitively expensive, largely because training algorithms takes a lot of computing power. For them, adding AI is simply a matter of keeping up with customers, who increasingly are looking for cost-effective ways of building machine learning into their software. At the AWS conference in Las Vegas earlier this year, the company showed off Amazon Cloud 9, an integrated development environment (IDE) that plugs directly into its cloud platform.
Microsoft and Xiaomi have signed a memorandum of understanding (MoU) to work closely in cloud computing, AI, and hardware. It has so far been uncommon for a US company to partner with a Chinese company on artificial intelligence, but it definitely makes sense as both countries are the biggest markets for those products and services.
In my last article, I discussed the evolution of Cloud Computing technology and how Cloud has been a paradigm shift for the Digital Transformation. Cloud provides the businesses with unheralded flexibility while offering them greater versatility and inexpensive solutions for managing the IT systems, where the technological developments are happening at a phenomenal pace and dynamic than ever before.
If you think back to Greek mythology, an Oracle was a person who provides wise counsel, prophetic predictions, or precognition of the future (as summarized from Wikipedia). So, it is somehow fitting that Oracle, the company, is the first to drive Artificial Intelligence (AI) and Machine Learning (ML) so broadly and deeply into our services. This push allows you to become your own oracle to drive insights, predictions, and customer/employee/partner interactions in ways that will pull you ahead of your competition.