Online securities firms in Japan are fighting for survival by diversifying services in the wake of a sharp drop in stock broker fees following their full liberalization 20 years ago. "The time when we could compete by lowering fees is over," said Rakuten Securities Inc. Brokerage fees were fully liberalized on Oct. 1, 1999, as one of the final steps in the financial system shake-up that was dubbed the "Big Bang." The reduction in fees then accelerated as online securities firms jumped into the market with the spread of the internet. In the year ended in March, the proportion of stock brokerage fees securities firms received stood at 0.03 percent of the Tokyo Stock Exchange's total trading value, which is one-tenth the 0.36 percent ratio posted in the year ended in March 1999, before liberalization took place. SBI Securities Co., the largest online brokerage, had 4.71 million trading accounts at the end of June, which is comparable with the number at Nomura Securities Co. "We've improved the quality of our services while making fees overwhelmingly low" for individual investors, said Yoshitaka Kitao, president and chief executive officer of SBI Holdings Inc., the parent of SBI Securities.

The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (#IROS2019) is being held in Macau this week. The theme this year is "robots connecting people". The conference accepted 1,127 papers for oral presentation, 148 late breaking news posters, and 41 workshops and tutorials. For those who can't make it in person, or can't possibly see everything, IROS is launching IROS TV, an onsite conference television channel featuring a new episode daily that is screened around the conference venue and online. The TV shows profile the research of scientists, educators, and practitioners in robotics, and provide an opportunity to learn about advances in robotics.

Greenhouses, search-and-rescue teams and warehouses are all looking for new methods to enable surveillance in a manner that is quick and safe for the objects and people surrounding them. Many of them already found their way into robotics, but wheeled ground-bound systems have limited maneuverability. Ideally it would be great if flying robots, a.k.a. A group or a swarm of them should be ideal to cover as much ground as possible. The price of fully autonomous MAVs usually comes with the cost of weight and size.

AI and machine learning are already changing the way that businesses operate. In the financial services sector, the technology is being used in everything from customer service to credit decisioning to fighting fraud. Earlier this year research from IHS Markit warned that banks piling into AI could spell tens of millions of job losses but the new IBM report suggests this is mistaken. The MIT-IBM Watson AI Lab used machine learning to analyse 170 million online job postings in the US between 2010 and 2017, finding that task are shifting between people and machines - but the change is slow. The overall demand for tasks that make up occupations are down between 2010 and 2017.

Practise makes perfect – it is an adage that has helped humans become highly dexterous and now it is an approach that is being applied to robots. Computer scientists at the University of Leeds are using the artificial intelligence (AI) techniques of automated planning and reinforcement learning to "train" a robot to find an object in a cluttered space, such as a warehouse shelf or in a fridge – and move it. The aim is to develop robotic autonomy, so the machine can assess the unique circumstances presented in a task and find a solution – akin to a robot transferring skills and knowledge to a new problem. The Leeds researchers are presenting their findings today (Monday, November 4) at the International Conference on Intelligent Robotics and Systems in Macau, China. The big challenge is that in a confined area, a robotic arm may not be able to grasp an object from above.

Artificial Intelligence (AI) and Machine Learning (ML) technologies have changed the way both offline and e-retailers interact with or approach the customer and the way they offer products, particularly in fashion eretail. AI and ML are allowing for critical insights into more accurate personalisation of products and services. Customer experiences can now be data-driven all thanks to insights that we receive through the implementation of AI and ML. These technologies allow pattern recognition of each consumer to match their preferences to the product offerings. Robust recommendation engines based on ML can boost up-sell and cross-selling opportunities – directly affecting the top and bottom line.

Hirotada Ototake, a Japanese writer born without arms and legs who is best known for his 1998 memoir "No One's Perfect," has embarked on a project to develop electrically operated "robot" legs that will enable him and others like him to walk. The 43-year-old Ototake, who writes about his challenge in a new book released on Friday, said he hopes to give hope to people who have lost their legs due to disabilities or accidents. Recently in Tokyo, Ototake took another try at walking with the devices, which were developed by Sony Computer Science Laboratories Inc. Each one, including the shoe, weighs 5 kg. While attempting to keep his balance, he walked cautiously but twisted his face in exhaustion after finishing the exercise. "After much practice and walking just a short distance I can see that there are many difficulties in using this," Ototake said.

Companies today rely on data and analytics for driving personalization and to acquire and retain customers. The multifaceted technology can also tackle the growing problem of NPAs in the BFSI sector. Prahalad Thota, Head-Analytics and Data Science, Wells Fargo, in a conversation with ETCIO, highlights how analytics has come to be the business backbone. Edited excerpt: Are there any insights that you are deriving from data to guide consumer strategies or to get about with the regulatory environment? From a consumer strategy, personalization is a great example.

For many data scientists, the process of building and tuning machine learning models is only a small portion of the work they do every day. The vast majority of their time is spent doing the less-than-glamorous (but crucial) work of performing ETL, building data pipelines, and putting models into production. Along the way, we'll demonstrate how Delta Lake is the ideal platform for the machine learning life cycle because it offers tools and features that unify data science, data engineering, and production workflows, including: These features of Delta Lake allow data engineers and scientists to design reliable, resilient, automated data pipelines and machine learning models faster than ever. A common architecture uses tables that correspond to different quality levels in the data engineering pipeline, progressively adding structure to the data: data ingestion ("Bronze" tables), transformation/feature engineering ("Silver" tables), and machine learning training or prediction ("Gold" tables). Combined, we refer to these tables as a "multi-hop" architecture.

As the field of data science explodes, data professionals are increasingly using programming language Python to get work done, over other tools such as R and SQL, according to Harnham's US Data & Analytics Salary Guide 2019, released this week. With more professionals entering the field, the data science industry is moving from traditional "core" data scientists toward those with more specialized skillsets, the report noted. And in an active market, candidates are often able to be selective about the jobs they take. Data scientists move between roles more quickly than professionals in any other part of the tech industry, averaging less than two years in each position, the report found. SEE: Python is eating the world: How one developer's side project became the hottest programming language on the planet (cover story PDF) (TechRepublic) When it comes to tools, a common debate in the data science realm is whether or not Python or R is a better programming language for data work.