Collaborating Authors

BMW explores in-car subscriptions for smart, connected services


When we purchase a new car in today's market, it is often the case that you can choose to add a personal touch for an additional fee: seat warmers, a particular color, wheel rims, and more. However, once the car leaves the showroom and is in a customer's hands, beyond repairs or, perhaps, a rare upgrade, there is little room for manufacturers to generate extra revenue from a sale. With the arrival of mobile solutions, the Internet of Things (IoT), and in-car connectivity, however, the game changed -- and the vehicle industry is now able to capitalize on the same subscription-based models that others, such as streaming content providers, are already shifting to. BMW appears to be keen to cash in on this change in consumer trends. This week, the automaker announced a set of new services in tandem with its upgraded vehicle operating system, OS version 7, including what could become subscription-based bolt-ons for owners.

Virtual humanoid RoboCup workshops


The 24th edition of RoboCup, due to take place in Bordeaux in late June, has been postponed until 2021. Obviously an event which centres on soccer matches between opposing teams of robots is something that cannot be recreated online. However, keen to do something in place of the annual extravaganza, the organisers laid on a virtual workshop for the RoboCup humanoid community. This provided a venue for teams to present updates, discuss ideas and solve problems. The workshop took place from 25 to 28 June and included presentations, workshops and lightning talks.

Computer vision(CV): Leading public companies named


CV is a nascent market but it contains a plethora of both big technology companies and disruptors. Technology players with large sets of visual data are leading the pack in CV, with Chinese and US tech giants dominating each segment of the value chain. Google has been at the forefront of CV applications since 2012. Over the years the company has hired several ML experts. In 2014 it acquired the deep learning start-up DeepMind. Google's biggest asset is its wealth of customer data provided by their search business and YouTube.

New AI technique speeds up language models on edge devices


Researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and MIT-IBM Watson AI Lab recently proposed Hardware-Aware Transformers (HAT), an AI model training technique that incorporates Google's Transformer architecture. They claim that HAT can achieve a 3 times inferencing speedup on devices like the Raspberry Pi 4 while reducing model size by 3.7 times compared with a baseline. Google's Transformer is widely used in natural language processing (and even some computer vision) tasks because of its cutting-edge performance. Nevertheless, Transformers remain challenging to deploy on edge devices because of their computation cost; on a Raspberry Pi, translating a sentence with only 30 words requires 13 gigaflops (1 billion floating-point operations per second) and takes 20 seconds. This obviously limits the architecture's usefulness for developers and companies integrating language AI with mobile apps and services.

Data analytics fintech Galytix grabs investment


Data analytics fintech firm Galytix has secured investment for its recruitment and product development plans. There were no financial details about this post angel equity investment. The London-based firm offers an artificial intelligence platform and says it uses machine learning to ingest, curate and synthesise unstructured and structured data. Galytix CEO Raj Abrol says the company has got new board members and investors. A number will hold non-executive or advisory roles.

Technology Media and Telecoms (TMT) trends: Artificial Intelligence


Technology, media and telecoms (TMT) regulators will ponder rather than act on AI. Increased use of AI to generate deepfakes in the US presidential campaign may be the catalyst for substantive regulation. Debate regarding access to, and ownership of, data will continue with little regulatory change. For many industries, the focus will remain on operational efficiency. AI-based virtual assistants will gain significant traction.

What's Next After Machine Learning?


Recently, there is increasing attention towards machine learning and its application within personal and business contexts. "The study of computer algorithms that improve automatically through experience" Machine learning leverages mathematical models and big data to solve business problems. This requires two key support areas of data engineering and data science. Within data engineering, the evolution of cloud computing has allowed big data to be stored inexpensively. Within data science, the rise of data scientists and data science tools have allowed better ease of model building and exploration. However, machine learning is NOT the summit of a data analytics evolution journey.

How AI and IoT affects the manufacturing job market - The Manufacturer


Artificial Intelligence (AI) and smart devices are gaining more and more traction in the manufacturing market. AI can be used to automate multiple things, and the technologies behind it keep getting better, and smarter. And combining AI with the IoT means fewer people will be required to take decisions and to execute those decisions. If things keep evolving as they have been so far, one thing is certain: the manufacturing industry will never be the same. But, how can AI and IoT affect the manufacturing job market? How can they improve it?

The Impact of Artificial Intelligence on Human Rights


Adopting AI can affect not just your workers but how you deal with privacy and discrimination issues. As humans become more reliant on machines to make processes more efficient and inform their decisions, the potential for a conflict between artificial intelligence and human rights has emerged. If left unchecked, artificial intelligence can create inequality and can even be used to actively deny human rights across the globe. However, if used optimally, AI can enhance human rights, increase shared prosperity, and create a better future for us all. It is ultimately up to businesses to carefully consider the opportunities new technologies provide and how they can best leverage these opportunities while being conscious of the impact on human rights.

How artificial intelligence can save journalism


The economic fallout from the COVID-19 pandemic has caused an unprecedented crisis in journalism that could decimate media organizations around the world. The future of journalism -- and its survival -- could lie in artificial intelligence (AI). AI refers "to intelligent machines that learn from experience and perform tasks like humans," according to Francesco Marconi, a professor of journalism at Columbia University in New York, who has just published a book on the subject: Newsmakers, Artificial Intelligence and the Future of Journalism. Marconi was head of the media lab at the Wall Street Journal and the Associated Press, one of the largest news organizations in the world. His thesis is clear and incontrovertible: the journalism world is not keeping pace with the evolution of new technologies.