How can we protect, restore and promote sustainable operations and reduce our ecological footprint in both the private and public sectors? "AI is the easiest way to help the environment," claim experts in the first episode of the Analytics4Society podcast series. Listen in to learn more about the background to this claim. Experts also discuss how AI can help us enable 93% of the environmental goals outlined within the UN's 17 sustainable development goals. These goals provide a shared global blueprint to achieve a better and more sustainable future for all.

AI is no longer a futuristic pipedream. Across all sectors we see different branches of artificial intelligence enhancing product and service offering, from medical treatment to agricultural production. In the context of marketing, brands are increasingly allocating budgets to AI solutions that drive personalised advertising experiences. But when it comes to ROI, are some marketers missing a trick? Our recent research into AI investments in the UK and the US found that marketers are spending on average 15% of associated departmental budgets on AI.

Recogni, the designer of a vision-oriented AI program for autonomous vehicles, has secured US$25m in Series A financing led by GreatPoint Ventures with participation from Toyota AI Ventures, BMW i Ventures, Faurecia, Fluxunit and DNS Capital. Founded in 2017 and headquartered in San Jose, California, Recogni uses its Vision Cognition Processor to help autonomous vehicles process their surroundings in real time, using minimal power. "Today, autonomous vehicles have hit the processing efficiency wall and are unable to transition to Level 3 autonomy and beyond," said Ashwini Choudhary, co-founder and chief business officer, Recogni. "We're focused on creating high-performance and low-power AI processing to help make autonomous vehicles a reality. "Our system identifies small objects – like traffic lights – from over 200m (656ft) away in real time.

India ranks amongst the top three countries in the world in Artificial Intelligence (AI) skills, among top three in AI research, top nine when it comes to AI specialists, and top 20 on the AI Readiness Index. The demand for advanced AI-related technologies has the potential to unlock a trillion-dollar opportunity for India. Intel recognises that the complexity of bringing AI from model to real-world deployment takes a mix of established and novel hardware and software solutions and is committed to collaborating with the AI community to accelerate discoveries and make meaningful progress in how it uses AI to add value to our work and lives. Intel AI DevCamp (Intel AIDC) is one such effort to put AI tools into the hands of scientists, developers, analysts, and engineers. The event also serves as a showcase of innovation being driven in collaboration with industry leaders.

Check out the "Text, Language, and Speech" sessions at the O'Reilly Artificial Intelligence conference in London, 14-17 October 2019. Early price ends 6 September. As companies begin to explore AI technologies, three areas in particular are garnering a lot of attention: computer vision, natural language applications, and speech technologies. A recent report from the World Intellectual Patent Office (WIPO) found that together these three areas accounted for a majority of patents related to AI: computer vision (49% of all patents), natural language processing (NLP) (14%), and speech (13%). Companies are awash with unstructured and semi-structured text, and many organizations already have some experience with NLP and text analytics.

Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in our brain. Using the speed of modern computers and large data sets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience. Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artifical intelligence algorithms -- based on the very slow brain dynamics -- which outperform learning rates achieved to date by state-of-the-art learning algorithms. In an article published in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years. "The current scientific and technological viewpoint is that neurobiology and machine learning are two distinct disciplines that advanced independently," said the study's lead author, Prof. Ido Kanter, of Bar-Ilan University's Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center.

One of the two task forces announced to be formed by the US House Committee on financial services will be investigating the use of artificial intelligence technologies (AI) for FinTech. The focus of the task force will be to examine digital identification technologies using AI to reduce fraud. It will also look into issues such as regulating ML in the financial services industry, risks associated with algorithms & big data, and the impact of automation on jobs and the economy in the US. AI has been one of the hottest technologies used by emerging FinTech players. It is used in automation, social media analytics & intelligence tools, cybersecurity, fraud prevention, and other areas.

Researchers at Bar-Ilan University have said they have created a new type of ultra-fast artificial intelligence (AI) algorithm based on the dynamics of the human brain. According to a report published Friday in Scientific Reports, the algorithm demonstrates that, despite the human brain computing at a much slower rate than modern computers, it is extremely fast and efficient. As such, the scientists say, "Insights of fundamental principles of our brain have to be once again at the center of future artificial intelligence."

Bottom Line: The leading growth strategy for manufacturers in 2019 is improving shop floor productivity by investing in machine learning platforms that deliver the insights needed to improve product quality and production yields. Using machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing. According to a recent survey by Deloitte, machine learning is reducing unplanned machinery downtime between 15 – 30%, increasing production throughput by 20%, reducing maintenance costs 30% and delivering up to a 35% increase in quality. Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in) How the IIoT can change business models. How emerging technologies can transform the supply chain.

WASHINGTON DC: Researchers discovered an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. The new system, described in a study published in the journal'JAMA Network Open,' helped interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurate or better as experienced pathologists. "It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA. A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year. That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).