The graph represents a network of 3,439 Twitter users whose tweets in the requested range contained "datamining", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 10 May 2021 at 06:40 UTC. The requested start date was Monday, 10 May 2021 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 7-hour, 19-minute period from Monday, 26 April 2021 at 16:40 UTC to Monday, 10 May 2021 at 00:00 UTC.
Artificial intelligence grew by leaps and bounds over the years, leaving its footprint across different sectors, including marketing, healthcare, telecommunication, human resource, government, banking and what have you. The big companies are always on the lookout for new ways to upgrade their workflows. To that end, companies like Apple, Microsoft, Google and Facebook have embraced AI with open arms. Unlimited resources, budget, and market position allow big companies to drive innovations at warp speed. In contrast, small companies find AI beyond their paygrade.
This is the sixth, and final episode in a series dedicated to all things A.I. In this episode, Tae Royle, Head of Digital Products APAC from Ashurst Advance Digital is joined by Tara Waters, Partner and Head of Ashurst Advance Digital. This is the sixth and final episode in a series dedicated to all things Artificial Intelligence. My name is Tae Royle head of digital products from Ashurst did that digital and today I'm joined by Tara Waters partner and head of Ashurst Advanced Digital based out of our London office. Naturally we come to the question of what's next? In Lewis Carroll's second novel, Alice enters Wonderland by climbing through a mirror.
Because COVID-19 made it difficult for consumers to venture out and run their usual errands, FIs needed to find other ways to provide their services. The only way for them to really keep up with the speedy digitization was through the implementation of AI systems. To further discuss all things AI, PaymentsJournal sat down with Sudhir Jha, Mastercard SVP and head of Brighterion, and Tim Sloane, VP of Payments Innovation at Mercator Advisory Group. Jha believes that there were two fundamentally big changes that occurred in banking during the pandemic: the environment began constantly shifting, and person-to-person interactions were abruptly limited. "Every week, every month, there were different ways that we were trying to react to the pandemic," explained Jha.
It has been silent around this project since COVID-19 shook the world last year with seemingly few updates, until recently. Despite all this, the Matrix AI team have been working diligently behind the scenes and have once again started to garner worldwide attention. We believe that we should put more effort into introducing the Matrix AI Network to the general public in a more simplified form. For many people, whether they have a general interest or a stake, it can sometimes be difficult to grasp the vision of Matrix AI as a whole. In addition, it may take a certain technical understanding as well as patience to read and understand the Matrix White and Green Papers -- The first place you should refer to for a complete technical overview.
Pymetrics Founded by Harvard/MIT-trained PhDs, pymetrics uses neuroscience data and AI to help global clients like Unilever, Accenture and LinkedIn make their hiring and internal mobility more predictive and less biased. Using algorithms that are trained on high-performing employees at a company, pymetrics builds a trait profile of a company's top performers to select best fit talent. These algorithms are then audited to remove any gender or ethnic bias. With over 80 enterprise clients and offices in NYC, London, Sydney and Singapore, pymetrics is powering the future of hiring: efficient, predictive, and bias-free.
The widespread adoption of machine learning models in different applications has given rise to a new range of privacy and security concerns. Among them are'inference attacks', whereby attackers cause a target machine learning model to leak information about its training data. However, these attacks are not very well understood and we need to readjust our definitions and expectations of how they can affect our privacy. This is according to researchers from several academic institutions in Australia and India who made the warning in a new paper (PDF) accepted at the IEEE European Symposium on Security and Privacy, which will be held in September. The paper was jointly authored by researchers at the University of New South Wales; Birla Institute of Technology and Science, Pilani; Macquarie University; and the Cyber & Electronic Warfare Division, Defence Science and Technology Group, Australia.
Google has devised a machine learning (ML) model that predicts disk failures with 98 per cent accuracy. The idea is to reduce data recovery work when disks actually fail. According to a Google blog by technical program manager Nitin Agarwal and AI engineer Rostam Dinyari, Google has millions of hard disk drives (HDDs) under management, some of which fail. "Any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services." When a disk in Google's data centres encounters non-fatal problems, short of an actual crash, then data is (drained) read from the drive. The drive is then disconnected from production use, they apply diagnostics and it is fixed and returned to production.
Last week we discussed level 1 and 2 autonomy and this week we will move on to L3-L5 which is considered to be "true" autonomous driving. With L3 in certain situations (e.g., highway driving) the car can fully take over all driving tasks including lane changing, but the driver must be constantly paying attention and has to keep his/her hands near the steering wheel at all times and must be paying attention and not distracted by some other tasks such as watching tv, staring at a phone or sleeping. The reason the driver must always pay attention is that if the autonomous system finds itself in a situation it cannot handle (e.g., an unexpected detour or highway construction) it will provide a warning (e.g., seat vibrates or an alarm sounds) and then hand control back to the driver. L4 is a fully autonomous car that can perform all driving functions without fail and doesn't ever require intervention from the driver, though the driver has the option to take over at any time. The caveat however is that the autonomous function can ONLY be used in certain prescribed situations (e.g., proper weather conditions with certain visibility) or locations (e.g., in a well-mapped city or vicinity).