Tencent, Alibaba, Baidu and JD.com from China are in a global competition with Google/Alphabet, Apple, Facebook, Walmart and Amazon from the USA and SoftBank from Japan. All are agressively searching for talent, intellectual property, market share, logistics and supply chain technology, and presence all around the world. These leading tech-savvy companies have many things in common. Foremost, they are all in pursuit of global growth and the funding, technology and talent to propel that growth. And they all are investing in voice assistance and other forms of AI and robotics.
Deep-Learning-as-a-Service, unveiled at IBM's annual IT industry conference in Las Vegas, seeks to lower barriers to deploying AI and deep-learning tools, a complex and painstakingly repetitive process that requires large amounts of computing power, the company said. The new service allows companies to upload data in Watson Studio, IBM's cloud-native platform for data scientists, developers and business analysts. There, they can create deep-learning algorithms for datasets – known in AI parlance as a "neural network" – using a drag-and-drop interface to select, configure, design and code the network. IBM also has automated the repetitive process of fine-tuning deep-learning algorithms, with successive training runs started, monitored and stopped automatically. For many firms, the complexity of creating smart algorithms from scratch has kept them from leveraging AI to parse massive stores of data for business value, the company said.
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.
For newbies this is the best place to start; introductions, FAQs and a glossary of terms. Information on the different types of learning algorithms used in AI and ML systems and applications. A list of different software tools, used to simulate AI techniques, both free open source and commercial. A list of free data sets that can be used for research and testing of AI learning algorithms. Find out how different hardware can be used to host and accelerate the performance of AI applications.
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.
Artificial intelligence (AI) holds the promise of transforming both static and dynamic security measures to drastically reduce organizational risk exposure. Turning security policies into operational code is a daunting challenge facing agile DevOps today. In the face of constantly evolving attack tools, building a preventative defense requires a large set of contextual data such as historic actuals as well as predictive analytics and advanced modeling. Even if such feat is accomplished, SecOps still needs a reactive, near real-time response based on live threat intelligence to augment it. While AI is more hype than reality today, machine intelligence -- also referred to as predictive machine learning -- driven by a meta-analysis of large data sets that uses correlations and statistics, provides practical measures to reduce the need for human interference in policy decision-making.
One of the recent cover stories of'The Economist' emphasized on the importance that data has been gaining, stating "the world's most valuable resource is no longer oil, but data." Machine learning, which is bringing about the most dramatic advancements in artificial intelligence, is a data intensive technique. Lots of data is required to create, test and train the AI. As AI is gaining importance in the business world, so is data. AI is being leveraged by financial firms to advice customers on their investment choices, automakers are using it to build autopilot systems, and virtual assistants similar to Siri, Cortana are being introduced.
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AI is the construction of computers, algorithms and robots that mimic the intelligence observed in humans, such as learning, problem solving and rationalising. Unlike traditional computing, AI can make decisions in a range of situations that have not been pre-programmed into it by a human. Much of AI is about systems that can learn and evolve through experience, often to carry our specialised tasks such as driving, playing a strategy based game, or making investment decisions. This subset, also referred to as cognitive computing, needs to be trained by learning from experts. Looking to the future, the focus is on creating an Artificial General Intelligence (AGI) that can apply itself to a broad range of tasks in a much less structured way.
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.