Brokerages who use artificial intelligence could find opportunities to upsell based on changes in a client's lifestyle, according to a software vendor executive. The more data you feed a machine learning model and the more you train it, the better it gets, said Kevin Deveau, managing director of FICO Canada, part of San Jose, Calif.-based Fair Isaac Corp., in a recent interview. Artificial intelligence (AI) is when technology mimics human cognition such as learning from experience, identifying patterns and deriving insights, said Mark Breading, a partner with Boston-based Strategy Meets Action. Machine learning is a type of AI in which computers act without being explicitly programmed, SAS Institute Inc. notes. Bigger brokerages with enough money to invest in AI and machine learning could use those technologies to build a "360-degree view" of a customer, said Deveau, in the context of how the COVID-19 pandemic is forcing companies to change the way they operate.
Palo Alto Networks has released next-generation firewall (NGFW) software that integrates machine learning to help protect enterprise traffic to and from hybrid clouds, IoT devices and the growing numbers of remote workers. The machine learning is built into the latest version of Palo Alto's firewall operating system – PAN 10.0 – to prevent real-time signatureless attacks and to quickly identify new devices – in particular IoT products – with behavior-based identification. NGFWs include traditional firewall protections like stateful packet inspection but add advanced security judgments based on application, user and content. "Security attacks are continually morphing at rapid pace and traditional signature-based security approaches cannot keep up with the millions of new devices, running a variety of operating systems and software stacks coming on the network," said Anand Oswal senior vice president and GM at Palo Alto. "IoT devices, which are growing exponentially, exacerbated that issue because they have so many of their own different agents, patches and OS's it's impossible to set security policies around them." Oswal said the ML in its new NGFW uses inline machine-learning models to identify variants of known attacks as well as many unknown cyberthreats to prevent up to 95% of zero-day malware in real time.
MOUNTAIN VIEW, Calif., June 4, 2020 – H2O.ai announced the availability of H2O Driverless AI integration, a leading automatic machine learning (AutoML) platform, with Snowflake, the Cloud Data Platform. This new integration enables Snowflake users to easily build and deploy ML models. Driverless AI automates the time consuming and demanding data science and machine learning workflows such as feature engineering, model tuning and model selection to achieve the highest predictive accuracy within the shortest time. The seamless integration of H2O Driverless AI with the Snowflake Cloud Data Platform is another step towards democratizing AI for all and empowering every company to be an AI company. "H2O.ai and Snowflake are in a unique position to help our customers adapt in the rapidly emergent business landscape and win with artificial intelligence on the cloud," said Sri Ambati, CEO and Founder of H2O.ai.
Google has been sued in the US over claims it illegally invades the privacy of users by tracking people even when they are browsing in "private mode". The class action wants at least $5bn (£4bn) from Google and owner Alphabet. Many internet users assume their search history isn't being tracked when they view in private mode, but Google says this isn't the case. The search engine denies this is illegal and says it is upfront about the data it collects in this mode. The proposed class action likely includes "millions" of Google users who since 1 June 2016 browsed the internet in private mode according to law firm Boies Schiller Flexner who filed the claim on Tuesday in federal court in San Jose, California.
PALO ALTO, Calif., May 6, 2020 – Cloudera (NYSE: CLDR), the enterprise data cloud company, today announced an expanded set of production machine learning capabilities for MLOps is now available in Cloudera Machine Learning (CML). Organizations can manage and secure the ML lifecycle for production machine learning with CML's new MLOps features and Cloudera SDX for models. Data scientists, machine learning engineers, and operators can collaborate in a single unified solution, drastically reducing time to value and minimizing business risk for production machine learning models. "Companies past the piloting phase of machine learning adoption are looking to scale deployments in production to hundreds or even thousands of ML models across their entire business," said Andrew Brust, Founder and CEO of Blue Badge Insights. "Managing, monitoring and governing models at this scale can't be a bespoke process. With a true ML operations platform, companies can make AI a mission-critical component of their digitally transformed business."
Nines, a Palo Alto-based teleradiology startup, received FDA 510(k) clearance for a system that can detect and triage two serious conditions from CT scans. The company's NinesAI system uses machine learning to identify potential cases of intercranial hemorrhage and mass effect, both of which are associated with strokes. The system then flags those cases for expedited review by radiologists. For both of these conditions, every hour counts toward a patient's survival. "This has been a long time coming. A lot of hard work went into it," CEO David Stavens said in a phone interview.
Earlier this week, a pair of sleek, four-wheeled robots began trundling across the cracked pavement outside the Sleep Train Arena, the defunct former home of the Sacramento Kings, which the state of California has turned into a field Covid-19 hospital. The robots, dubbed R2, were supposed to be delivering groceries to residents of a wealthy neighborhood in Houston, part of a rollout by the Mountain View, Calif.-based startup Nuro. Instead, like other robots the world over, they have been pressed into service delivering goods...
The ACM constitution provides that our Association holds a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--five Members-at-Large will be elected to serve on ACM Council. Please refer to the instructions posted at https://www.esc-vote.com/acm. To access the secure voting site, you will need to enter your email address (the email address associated with your ACM member record) and your unique PIN provided by Election Services Co. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 22 May 2020. Validation by the Tellers Committee will take place at 14:00 UTC on 26 May 2020. Elizabeth Churchill is a Director of User Experience at Google. Her field of study is Human Computer Interaction (HCI) and User Experience (UX), with a current focus on the design of effective designer and developer tools. Churchill has built research groups and led research in a number of well-known companies, including as Director of Human Computer Interaction at eBay Research Labs in San Jose, CA, as a Principal Research Scientist and Research Manager at Yahoo! in Santa Clara, CA, and as a Senior Scientist at the Palo Alto Research Center (PARC) and FXPAL, Fuji Xerox's Research lab in Silicon Valley. Working across a number of research areas, she has over 100 peer reviewed top-tier journal and conference publications in theoretical and applied psychology, cognitive science, human-computer interaction, mobile and ubiquitous computing, computer-mediated communication, and social media, more than 50 patents granted or pending, and 7 academic books. Her team produces research that impacts a large number of Google's products (by shaping Google's Flutter and Material Design), influencing the work of hundreds of thousands of designers and developers globally, and thus affecting the user experience of millions of end-users. She continues to guest lecture at universities and to mentor early stage career professionals and students.
Gary Marcus, founder and CEO of Robust.ai, a company based in Palo Alto, Calif., that is trying to build a cognitive platform for a range of bots, is a proponent of AGI having to work more like a human mind. Speaking at the MIT Technology Review's virtual EmTech Digital conference, he said today's deep learning algorithms lack the ability to contextualize and generalize information, which are some of the biggest advantages to human-like thinking. Marcus said he doesn't specifically think machines need to replicate the human brain, neuron for neuron. But there are some aspects of human thought, like using symbolic representation of information to extrapolate knowledge to a broader set of problems, that would help achieve more general intelligence. "[Deep learning] doesn't work for reasoning or language understanding, which we desperately need right now," Marcus said.
Nuro, the self-driving startup founded by two ex-Google engineers, was approved to test its driverless delivery robots on public roads in California. The company is the second to receive a driverless permit in the state. Nuro, which has tested its driverless grocery delivery service in Arizona and Texas, is authorized to test two light-duty delivery vehicles in nine Bay Area cities, according the California DMV. This includes portions of the cities of Atherton, East Palo Alto, Los Altos, Los Altos Hills, Menlo Park, Mountain View, Palo Alto, Sunnyvale, and Woodside. The vehicles can't exceed 25 mph and are only approved to operate in fair weather conditions on streets with a speed limit of no more than 35 mph.