The modern foundations of competition law and policy – with a focus on competitive pricing for consumers rather than a broad and diverse set of social and economic issues – are being shaken by the dual tremors of populism and the digital economy. This is occurring in major jurisdictions such as the European Union and the US, and tremors have also been felt in Canada. But do these two developments represent seismic threats to competition law and policy? That question has been the subject of recent debate in Canada and elsewhere in the world. Populist concerns about income disparities and inclusive growth have led some to call for updating antitrust/competition law.
For many people, Google is simply the gateway to a vast archive of facts and memories. For those who pay closer attention to its business dealings, the company also invests billions to find new ways to use the power of computers: it's developing robots, virtual reality gear and self-driving cars. Remember all the hubbub about Google Glass? Google has been using the same approach in sustainability – spreading its wealth in a variety of projects to cut its waste and carbon footprint, initiatives which may one day generate profits. During the SXSW Eco conference this week, I caught up with Google's sustainability officer, Kate Brandt, to find out more.
"Legal confidentiality is a shield for citizens." These are the words of Shami Chakrabarti, the one-time director of the UK-based human rights group Liberty, who was speaking in 2018. Well, it seems that this shield has just been broken, because researchers at the University of Zurich in Switzerland have published a study in which they were able to identify the participants in confidential legal cases, even though such participants had been anonymized. By harnessing these technologies in tandem, the study's authors could mine over 120,000 public legal records and then use an algorithm to identify connections between them. Described as "linkage," this process enabled the researchers to identify anonymous parties mentioned in public records of Swiss Supreme Court decisions, simply by linking anonymous records to those where various pieces of information was given.
'Insider Threat' is a formidable risk to business because it threatens both customer and employee trust. Accidental or malicious misuse of a firm's most sensitive and valuable data can result in customer identity theft, financial fraud, intellectual property theft, or damage to infrastructure. Because insiders have privileged access to data in order to do their jobs, it's usually quite difficult for security professionals to detect suspicious activity; a process even more challenging to automate (and deploy at scale across the large organisations that most need it) as – so I will suggest – computers fundamentally lack semantic understanding of the meaning of the'bits' they so adroitly process. Conversely, in this talk I will outline a new approach to'Insider Threat' detection that draws inspiration from the Traffic Analysis' of encrypted Axis signal traffic' undertaken at Bletchley Park in WW2. A novel approach that (i) conceives companies as complex autonomous autopoietic entities and (ii) deploys state of art computational analysis of the communication flows that so define the company to flag potentially aberrant employee behaviour; intelligence that can be leveraged to help detect HR problematics before they arise.
The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS Analytics Product Manager. "This software helps provide positive outcomes to increase profitability, better understand customer behavior and decrease the cost of doing business." SAS Viya SAS Visual Data Mining and Machine Learning is one of the initial analytics applications on the SAS Viya platform. SAS Viya is an innovative analytics environment designed for use in the cloud that provides the power of SAS Analytics through SAS interfaces as well as open APIs for Python, Lua, Java and REST. The new analytics offerings for SAS Viya are structured for a diverse range of users, while maintaining consistency and manageability. In addition to SAS Visual Data Mining and Machine Learning for data scientists, the Viya family will include SAS Visual Analytics for business analysts and SAS Visual Statistics, aimed at experienced statistical users. The breadth of SAS Viya applications will satisfy the appetites of all user types, while maintaining a consistent structure. The speed of the multithreaded parallel processing engine in SAS Viya will drive faster decisions. And the strength of analytics from the advanced analytics leader will produce trusted results. To better understand the need, applications and benefits of machine learning, please visit Machine Learning: what it is and why it matters. Today's announcement was made at the Analytics Experience conference in Las Vegas, a business technology conference presented by SAS that brings together more than 10,000 attendees on-site and online to share ideas on critical business issues. About SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 80,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW . SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. Other brand and product names are trademarks of their respective companies.