MUMBAI: A report published by India's largest software exporter, Tata Consultancy Services, in association with Confederation of Indian Industry, found that despite the evolution of patent laws, the increasing proliferation of artificial intelligence across the world necessitates new policies for the enforcement of intellectual property rights. "Current patent laws treat AI software inventions as logical algorithms implemented on the computer. While patent eligibility of algorithms is valid, there is little about how to deal with inventions that are heuristic in nature," the report found. In artificial intelligence a'heuristic' is a technique used to solve a problem faster than classic methods. Software is no longer limited to traditional rule-based systems and has increasingly turned heuristic, showing higher intelligence over rule-based systems, it cited.

Rob Roy, public sector chief technology officer at Micro Focus Government Solutions, has said machine learning and cloud platforms could help government agencies protect their networks from cybersecurity breaches and achieve efficiency. Roy wrote how unsupervised machine learning could assist agencies in detecting anomalous user behavior. "Unlike the rules-based approach, unsupervised machine learning lets the technology develop an understanding of how the network's users typically behave and alert administrators when something abnormal occurs, increasing the likelihood that a rogue event is detected and a response is orchestrated at machine speed," he added. He said data scientists could help agencies sort through raw data and determine relevant information to analyze in order to address a specific problem. He also mentioned the potential benefits of migrating common business-oriented language-based mainframe applications and other legacy systems running mission-critical functions to the cloud.

MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man. A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots. The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant. Most recently, HSBC said that the bank was using the technology to combat financial crime.

Machine Learning is undeniably one of the most influential and powerful technologies in today's world. More importantly, we are far from seeing its full potential. There's no doubt, it will continue to be making headlines for the foreseeable future. This article is designed as an introduction to the Machine Learning concepts, covering all the fundamental ideas without being too high level. Machine learning is a tool for turning information into knowledge.

Technology has played a key role in retail for decades, from early innovations like barcode scanning and digital point of sale devices, to the global frontier of modern logistics. Through it all, however, the fundamentals remain the same: retailers generate huge quantities of data, face unpredictable environments, and need to continually adapt to the ever-evolving needs of the customer. Throw in the chaos of Black Friday and Cyber Monday, and you've got one of the most complex enterprise challenges in the world. It's also a challenge tailor-made for AI: a technology that thrives on big data, adapts to change fluidly, and can deliver personalized experiences at scale. With the holiday rush upon us, let's take a look at how two Cloud AI customers--3PM for online shoppers and Tulip for in-store--are helping make retail more efficient, more personal, and more trustworthy.

Junning Huang* 1, Sirui Xie* 2, Jiankai Sun 4, Qiurui Ma 3, Chunxiao Liu 1, Jianping Shi 1, Dahua Lin 4, Bolei Zhou 4 Abstract -- Classical autonomous driving systems are mod-ularized as a pipeline of perception, decision, planning, and control. The driving decision plays a central role in processing the observation from the perception as well as directing the execution of downstream planning and control modules. Commonly the decision module is designed to be rule-based and is difficult to learn from data. Recently end-to-end neural control policy has been proposed to replace this pipeline, given its generalization ability. However, it remains challenging to enforce physical or logical constraints on the decision to ensure driving safety and stability. In this work, we propose a hybrid framework for learning a decision module, which is agnostic to the mechanisms of perception, planning, and control modules. By imitating the low-level control behavior, it learns the high-level driving decisions while bypasses the ambiguous annotation of high-level driving decisions. We demonstrate that the simulation agents with a learned decision module can be generalized to various complex driving scenarios where the rule-based approach fails. Furthermore, it can generate driving behaviors that are smoother and safer than end-to-end neural policies ‡ .

Data: As with all ML applications, quality data is foundational to building anti-fraud ML systems. Data sets are only growing larger, and as the volumes increase, so does the challenge of detecting fraud. Thankfully the adage that more data equals better models is true when it comes to fraud detection. The make-or-break factor is having a ML platform that can scale as data and complexity increase. Multiplicity: There's no single ML algorithm or method that works best for fraud detection.

SINGAPORE - Singapore-based regulatory technology firm Tookitaki has raised US$19.2 million (S$26.1 million) in Series A funding as it seeks to expand its presence in international markets. The company has received $11.7 million in investment, adding to the $7.5 million raised earlier this year. A group, led by Viola Fintech and SIG Asia Investment, was responsible for the fresh injection of funds, which will help Tookitaki increase its employee headcount across its three offices in Singapore, India and the United States by up to 100 per cent, as well as to fine-tune its products. "Our vision has always been for our compliance technology to become globally accepted by financial institutions around the world, and (the investments) put us in a better place to deliver on that vision," said Tookitaki co-founder and chief executive officer Abhishek Chatterjee on Monday (Nov 25) Tookitaki offers two artificial intelligence-powered software platforms. The first is an anti-money laundering solution that aims to help banks better monitor and detect suspicious transactions, and comply with regulatory requirements.

Early December marks the kickoff of Amazon's AWS re:Invent conference in Las Vegas, and ahead of the festivities the tech giant has unveiled a slew of product enhancements. To this end, Amazon Translate, the company's cloud machine translation service that delivers language translation via API requests, today gained new languages and variants and expanded to new regions globally. By way of a refresher, Translate -- which debuted in preview in November 2017 ahead of general availability last April -- taps AI that aims to deliver more accurate and natural-sounding translation than statistical or rule-based approaches. It allows customers to define how brand names, character names, model names, and other unique terms get translated. When used in tandem with a natural language processing app, Translate also facilitates sentiment analysis.

T eaching Perception Jonathan H. Connell 1 Abstract -- The visual world is very rich and generally too complex to perceive in its entirety. Y et only certain features are typically required to adequately perform some task in a given situation. Rather than hardwire-in decisions about when and what to sense, this paper describes a robotic system whose behavioral policy can be set by verbal instructions it receives. These capabilities are demonstrated in an associated video [1] showing the fully implemented system guiding the perception of a physical robot in simple scenario. The structure and functioning of the underlying natural language based symbolic reasoning system is also discussed. I. INTRODUCTION Sensing is not without costs. For any given object there are many things that can be known about it. What constitutes a reasonable amount of information to obtain? For instance, to identify an object in a scene a robot could run a DNN recognizer. But, depending on the resources available, this may take a noticeable amount of time. And, while some recognizers have Nary outputs, others are designed as one-versus-all. In this case, to classify an object a robot might have to run N separate nets.