Artificial intelligence (AI) processing today is mostly done in a cloud-based data center. The majority of AI processing is dominated by training of deep learning models, which requires heavy compute capacity. In the last 6 years, the industry has experienced a 300,000X growth in compute requirements, with graphics processing units (GPUs) providing most of that horsepower. According to a new report from Tractica, however, as the diversity of AI applications grows, an increasing amount of AI processing will be handled within edge devices rather than in a centralized, cloud-based environment. Tractica forecasts that AI edge device shipments will increase from 161.4 million units in 2018 to 2.6 billion units worldwide annually by 2025.
Amazon caused a few gasps at its Alexa event Thursday when it revealed it had 70 things to announce. If that sounds like a lot to take in, fret not -- not all of those were hardware announcements. We've rounded up all of the Alexa-infused products Amazon showcased, including speakers, an in-car device and, um, a microwave? The revamped Echo Dot has a new look, with rounded edges, and packs more punch, with a 1.6-inch driver delivering 70 percent louder audio than the previous model. Amazon says the speaker has lower distortion and enhanced bass.
Neo4j, the market leader in connected data, announced today the upcoming release of Neo4j 3.5, the native graph platform designed to drive the success and adoption of real-time business applications, including artificial intelligence (AI) and machine learning (ML) systems. Neo4j customers – including eBay and Caterpillar – have demonstrated that connected graph datasets are a foundational element of enterprise AI applications. Graph-based data models provide the necessary context for AI applications by capturing facts related to and relationships among people, processes, applications, data and machines. Informed by successful AI customer deployments – including knowledge graphs, fraud detection, recommendation systems and conversation engines – Neo4j 3.5 delivers foundational features for AI-powered systems of connection to generate bottom-line business value. "The way we organize and represent knowledge in AI-powered systems has a profound effect on what and how they can learn," said Bowles.
They were all founded in 2005 or earlier, but it wasn't until the past few years that they took off after hitting on their current business automating simple back-office tasks and dubbing it "robotic process automation." UiPath on Monday completed a new funding round at a $3 billion valuation, said a person familiar with the process, six months after a prior round valued it at $1.1 billion. In July, rival Automation Anywhere raised its first round of financing at a $1.8 billion valuation. Shares in Blue Prism, a public company in the U.K., have risen nearly 30 times since they were listed in March 2016. It raised about $60 million in a secondary share sale in January.
According to the report, the global machine learning chip market was valued at $2,425.6 million in 2017, and is projected to reach $37,849.8 million by 2025, registering a CAGR of 40.8% from 2018 to 2025. The trend in artificial intelligence (AI), use of machine learning in numerous applications and emergence of quantum computing are the factors which increase the demand for machine learning chip market. In addition, the development of autonomous robots that can control themselves without human intervention is anticipated to provide potential growth opportunities for the market. However, dearth of skilled workforce and AI phobia are the major restraints of the market. Moreover, increase in demand for smart homes & cities, rise in efforts to make more human-like robots and popularity of IoT across the globe are expected to create tremendous opportunities for the market expansion.
As businesses struggle to combat increasingly sophisticated cybersecurity attacks, the severity of which is exacerbated by both the vanishing IT perimeters in today's mobile and IoT era, coupled with an acute shortage of skilled security professionals, IT security teams need both a new approach and powerful new tools to protect data and other high-value assets. Increasingly, they are looking to artificial intelligence (AI) as a key weapon to win the battle against stealthy threats inside their IT infrastructures, according to a new global research study conducted by the Ponemon Institute on behalf of Aruba, a Hewlett Packard Enterprise company HPE, 1.66% This press release features multimedia. The Ponemon Institute study, entitled "Closing the IT Security Gap with Automation & AI in the Era of IoT," surveyed 4,000 security and IT professionals across the Americas, Europe and Asia to understand what makes security deficiencies so hard to fix, and what types of technologies and processes are needed to stay a step ahead of bad actors within the new threat landscape. The research revealed that in the quest to protect data and other high-value assets, security systems incorporating machine learning and other AI-based technologies are essential for detecting and stopping attacks that target users and IoT devices.
WIRE)--Imanis Data, the leader in enterprise data management powered by machine learning, today announced a major upgrade to the Imanis Data Management Platform, continuing the company's momentum since raising $13.5 million Series B funding earlier this year. The new Version 4.0 includes multiple industry firsts including autonomous backup, any-point-in-time recovery for multiple NoSQL databases, enhanced ransomware prevention, as well as numerous Imanis Data management enhancements. Hadoop and NoSQL applications are running in virtually every enterprise on-premises and in the cloud, but they lack enterprise data management capabilities, exposing organizations to data loss, downtime, and cyberattacks. "According to our research, 78% of organizations currently use NoSQL databases and an additional 18% plan to in the future," said Christophe Bertrand, senior analyst for data protection, at ESG Research. "The data protection market in this space is underserved by traditional vendors and Imanis Data with their unique machine learning approach is setting the bar for Hadoop and NoSQL enterprise data management."
TORONTO, Sept. 18, 2018 – Datametrex AI Limited (the "Company" or "Datametrex") (TSXV: DM, FSE: D4G) is pleased to announce that it has created a newly formed subsidiary, Canntop AI Inc. ("Canntop AI"), to focus on the global cannabis industry. This follows on the previous announcement by the Company to establish a working relationship with a key data collection company in the Agriculture and Cannabis sectors. Canntop AI will utilize Nexalogy's tool set and unique algorithms to analyze large unstructured data sets from the Agriculture and Cannabis space. By gathering and analyzing the data we will be able to provide cultivators and dispensaries with business intelligence that allows them to better understand their clients and markets in the areas of patient analysis, strain and protocol management, and quality assurance. "Taking our proven solutions and focusing them on this sector will add tremendous value to growers, dispensaries, pharma and government agencies.
Digital transformation is a process that has been underway for some time. It is a wide-ranging overhaul of the way organisations do business both internally and externally. It includes changes to technology and culture from users to developers, operations teams to the C-Suite. On the technology side, organisations have taken advantage of APIs to integrate with suppliers and key customers. This removes paper from the workflow reducing both delays in entering data and the risk of keying errors.
The maverick of personal computing is looking for its next big thing in spaces like healthcare, AR, and autonomous cars, all while keeping its lead in consumer hardware. With an uphill battle in AI, slowing growth in smartphones, and its fingers in so many pies, can Apple reinvent itself for a third time? Get the detailed analysis on Apple's trove of patents, acquisitions, earnings calls, recent product releases, and organizational structure. In many ways, Apple remains a company made in the image of Steve Jobs: iconoclastic and fiercely product focused. But today, Apple is at a crossroads. Under CEO Tim Cook, Apple's ability to seize on emerging technology raises many new questions. Looking for the next wave, Apple is clearly expanding into augmented reality and wearables with the Apple Watch and AirPods wireless headphones. Apple's HomePod speaker system is poised to expand Siri's footprint into the home and serve as a competitor to Amazon's blockbuster Echo device and accompanying virtual assistant Alexa. But the next "big one" -- a success and growth driver on the scale of the iPhone -- has not yet been determined. Will it be augmented reality, auto, wearables? Apple is famously secretive, and a cloud of hearsay and gossip surrounds the company's every move. Apple is believed to be working on augmented reality headsets, connected car software, transformative healthcare devices and apps, as well as smart home tech, and new machine learning applications. We dug through Apple's trove of patents, acquisitions, earnings calls, recent product releases, and organizational structure for concrete hints at how the company will approach its next self-reinvention. Given Apple's size and prominence, we won't be covering every aspect of its business or rehashing old news. There's strong evidence Apple is once again actively "cannibalizing itself," putting massive resources behind consumer tech that will render its own iPhone obsolete.