Before Insurance Australia Group (IAG) can begin selling more than insurance products to consumers, the company realised it needed to shift what is currently a very analogue business into something that is more digitally orientated. "We really want to change the mindset, get some records and customer value, and build new businesses as well, including beyond insurance," IAG Digital Architecture director Ian Jamieson explained at New Relic Future Stack 2019 last week. "An insurance company not selling insurance is quite disruptive because a lot of the core systems is under insurance, so if we want to sell a solution that provides emergency assistance to your home that is not an insurance product, how would you bill someone for that so that it's not under insurance … there are a range of things we need to transform and add new capabilities to." Jamieson said some areas that IAG is looking to expand its business into include motor and home repair services, spinning up brand new businesses such as in mobility services, and through acquisitions of startups, such as its most recent purchase of Carbar, a subscription-based car ownership platform. To make sure these business plans become a reality, Jamieson said the company has moved away from taking traditional waterfall approaches to projects and using a cross-functional method.

The incorporation of AI technology in the construction industry has facilitated better safety, higher efficiency, and lower downtime. FREMONT, CA: The construction industry has not completely realized the potential of artificial intelligence (AI). It is under-digitized when compared to other sectors, and lags in efficiency and performance. The incorporation of AI capabilities will not only drive higher growth but will also enable the organizations to optimize their operations. Even though the applications of AI in the construction industry are nascent, it is gaining traction because of the robust benefits offered by the technology. AI can streamline and simplify the design process through integration with robust technologies such as 3D planning and building information modeling (BIM).

Scientists have used artificial intelligence to predict how cancers will progress and evolve, which could help doctors design the most effective treatment for each patient. A team led by the Institute of Cancer Research, London (ICR) and the University of Edinburgh developed a new technique known as Revolver (Repeated evolution of cancer), which picks out patterns in DNA mutation within cancers and uses the information to forecast future genetic changes. They said the ever-changing nature of tumors is one of the biggest challenges of treatment – with cancers often evolving into a drug-resistant form. But if doctors can predict how a tumor will evolve, they could intervene earlier to stop cancer in its tracks before it has had a chance to evolve or develop resistance, increasing the patient's chances of survival. The team also found a link between certain sequences of repeated tumor mutations and survival outcomes.

You've read it in the papers. You've experienced it in life. And they are doing it fast. Siri turned 9 on October 4 (go ahead, ask her if you don't believe me). Tesla's first Autopilot program is also 9.

The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products. The benefit of the Pix2Pix model is that compared to other GANs for conditional image generation, it is relatively simple and capable of generating large high-quality images across a variety of image translation tasks. The model is very impressive but has an architecture that appears somewhat complicated to implement for beginners. In this tutorial, you will discover how to implement the Pix2Pix GAN architecture from scratch using the Keras deep learning framework. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code. How to Implement Pix2Pix GAN Models From Scratch With Keras Photo by Ray in Manila, some rights reserved.

Not all tech billionaires are advocates of artificial intelligence (AI). Some are so worried about the effects AI is having on society that they are spending their billions trying to monitor it. This, in turn, has created a new frontier in philanthropy. For Pierre Omidyar, the founder of eBay, AI is such a concern that last year he set up Luminate, a London-based organization that advocates for civic empowerment, data and digital rights, financial transparency, and independent media. Pierre Omidyar, the founder of eBay, has supported monitoring artificial intelligence.

Umbo Computer Vision, an artificial intelligence (AI) startup that offers autonomous video security systems to businesses, has raised $8 million in what it's calling a "post-A" round of funding. The round was co-led by Translink Capital and Susquehanna International Group, with participation from Shin-Kong Capital and Shin-Kong Security, among other existing investors. Founded in 2014, Taiwan- and San Francisco-based Umbo develops software, hardware, and AI smarts that can detect and identify human behaviors related to security, such as intrusion, tailgating (when an unauthorized individual follows someone into private premises), and wall-scaling. In addition to its hubs in Taipei, Taiwan and San Francisco, Umbo recently opened its first European office in the U.K., a country renowned for its love affair with security cameras. Umbo's offering incorporates three components.

The French national radiology congress (JFR) kicked off officially on Friday with a strengthened focus on artificial intelligence (AI) and machine learning. The discussion included an update on an ambitious project to create a national ecosystem in France for AI algorithm development. Dr. Jean-Paul Beregi, head of imaging at Nȋmes university hospital, updated delegates on the current status of France's new DRIM France IA imaging initiative, a nonprofit, independent AI ecosystem. The project has advanced significantly since it was announced last October, according to Beregi. The ecosystem now has a scientific committee and a charter, and is looking at how to move forward with institutional partners.

ShotSpotter (Nasdaq: SSTI), a gunshot detection, location and forensic analysis provider, announces the U.S. Patent and Trademark Office (USPTO) has granted the company U.S. Patent No. 10,424,048 entitled "Systems and Methods Involving Creation and/or Utilization of Image Mosaics in Classification of Acoustic Events." ShotSpotter's real-time gunshot detection solution uses a two-step process that employs both machine classification and human review. The system can distinguish with high accuracy whether a loud, impulsive sound detected by its acoustic sensors is a gunshot or a non-gunshot incident, such as fireworks, in less than 60 seconds, according to the company. The innovation behind the patent granted to ShotSpotter covers the conversion of multiple features of the audio event into a set of visual displays that are combined into a single image mosaic. This enables the system to leverage deep learning neural networks that typically identify and classify images, not sounds.

By Rachel Thomas, Co-founder at fast.ai Goodhart's Law states that "When a measure becomes a target, it ceases to be a good measure." At their heart, what most current AI approaches do is to optimize metrics. The practice of optimizing metrics is not new nor unique to AI, yet AI can be particularly efficient (even too efficient!) This is important to understand, because any risks of optimizing metrics are heightened by AI.