Lenovo offers surveillance security solutions at Intersec 2020

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Video surveillance systems are evolving and are using artificial intelligence (AI) to inspect and analyse video footage, interpret patterns and flag unusual activity. Lenovo DCG and Pivot3 provide a state-of-the-art upgraded infrastructure solutions that aim to enhance current technology required to support these systems rather than entrusting the preservation of crucial data to outdated NVR technology. Commenting on the partnership, Dr. Chris Cooper, General Manager for Lenovo DCG, Middle East, Turkey and Africa, said, "We are delighted to showcase our partnership with Pivot3 at one the world's leading technology trade shows. The Middle East is exhibiting tremendous growth in terms of adopting smart solutions. The UAE in particular is investing heavily in implementing the latest innovations in their technological infrastructure; therefore, we see great potential from our partnership with Pivot3 as we work together to supply the appetite for next generation computing products and services."


Adventures With Artificial Intelligence and Machine Learning

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Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a "data scientist in a box". I'll start by saying that machine learning and'artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways. The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a naïve Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didn't have real data and only had a conceptual problem to be solved.


Introducing the Temporal data set, a benchmark for recognizing actions in videos

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A new data set to train and benchmark AI systems to better understand actions in videos -- in particular, actions that can't be determined by viewing just a single frame. Current video data sets often focus on actions where a single image is enough for recognition, such as washing dishes, eating pizza, or playing guitar. To improve computer vision systems' understanding of elements that can be recognized only in a video sequence -- such as whether someone is sneezing or opening a door -- we discovered a set of actions where temporal information is essential for recognition. We're now sharing this work, along with our methodology for determining those classes and results from training networks on it, in order to help researchers benchmark their systems' ability to recognize temporal actions. To discover which actions in video should be designated as temporal classes, we presented annotators with video clips from existing video recognition data sets, with their frames shuffled out of order.


Digital Transformation: 2020 and beyond

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Every definition of transformation includes the notion of change. The most common examples are found in nature, in the process of change from caterpillar to butterfly and tadpole to frog. Ultimately transformation is the process of moving from one state to another. Processes are often expressed in terms of phases of varying lengths during which critical events occur. Digital transformation is no exception.


Artificial Intelligence and Schrödinger's cat

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I doubt that there has ever been a time in human history when technologies had really entered the public discourse at the scale that we see today, and that brings forth a very interesting dynamics worth musing over as we are getting really keen on using it as a game-changer that will change the very nature of human existence. Couple of days back, I had the pleasure of interacting with a set of politicians over the concept of Artificial Intelligence (AI) and I am happy to report that they knew what AI is, but also unhappy to report that their interest in knowing more about AI was mainly to see if it can help them win elections. I must admit that these new-fangled technologies may or may not be working, but they have acquired the brand equity similar to that of a magic wand. Most people, more so when are over fifty like the set of politicians I met, think that it is possible to find wizkids who can perform miracles using computers. When I was explained the idea of applying machines to identify patterns from the data, they were quick to grasp the concept, and they were savvy enough to work out that their advantage was in finding out the pattern followed by voters.


Technology predictions for 2020 – the impact of AI in the legal sector

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The legal sector is quickly moving to embrace digital transformation and leaning towards innovation as it recognises the opportunity to improve customer services, drive productivity and adhere to the raft of compliance checks that all law firms have to meet. In fact, in feedback from legal professionals in our recent Advanced Trends Survey Report 2019/2020, only 40 per cent felt their law firm wasn't acting fast enough to keep up with the pace of technology innovation – so that means 60 per cent are acting with pace and are certainly well ahead on that journey. To encourage greater innovation, one technology that we predict will have a transformative effect on the industry is Artificial Intelligence (AI). Although AI is still in its relative infancy, it is already helping to change the way many industries operate and the legal sector is increasingly recognising its potential benefits. For example, a recent Deloitte study estimated 100,000 legal roles will be automated by 2036, leaving legal professionals to concentrate on higher value, client facing tasks.


Apple's Latest AI Acquisition Is A Glimpse Into Future Devices

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Future Apple smart speakers, like HomePod, might be much more data privacy focused. Apple has reportedly paid $200 million to acquire Seattle-based artificial intelligence company Xnor.ai, The purchase is one of many for Apple, which has become adept at vacuuming up tech startups, but it also gives us a glimpse into the company's thinking when it comes to future devices. Xnor.ai's work on hyper-efficient, low-power AI that doesn't require powerful processing or a connection to the cloud (processing locally on-device instead), neatly slots into a few areas Apple is currently working on. Whilst Apple hasn't - and doesn't typically - comment on why it acquires certain companies and how they fit into its future roadmap, we can speculate on how Xnor.ai's work fits in to the master plan.


Google's Reformer: An Important Breakthrough of 2020

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Regardless of whether it's language, music, speech, or video, sequential data isn't simple for AI and machine learning models to understand, especially when it relies upon the extensive surrounding context. For example, if an individual or an item vanishes from view in a video just to return a lot later, numerous algorithms will overlook what it looked like. Researchers at Google set out to solve this with Transformer, a design that reached out to thousands of words, drastically improving performance in tasks like song composition, image synthesis, sentence-by-sentence text translation, and document summarization. In any case, Transformer isn't flawless by any stretch, extending it to bigger contexts makes clear its restrictions. Applications that utilize enormous windows have memory necessities going from gigabytes to terabytes in size, which means models can just ingest a few paragraphs of text or create short bits of music.


Machine Learning Tools Unlock the Most Critical Insights from Unstruc…

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Feel free to share but we would appreciate a Health Catalyst citation. Prof, Dept of Family Medicine, Indiana University School of Medicine This report is based on a 2018 Healthcare Analytics Summit presentation given by Shaun Grannis, MD, MS, FAAFP, FACMI, Director Regenstrief Center for Biomedical Informatics; Assoc. Prof, Dept of Family Medicine, Indiana University School of Medicine, entitled "Real-World Examples of Leveraging NLP, Big Data, and Data Science to Improve Population Health and Individual Care Outcomes." Feel free to share but we would appreciate a Health Catalyst citation. Many healthcare leaders operate on the premise that health system caregivers and stakeholders are more effective and better at what they do with the aid of thoughtful IT. This concept drives data analytics and technology integration in healthcare. But what does thoughtful IT mean? Thoughtful IT occurs when health systems use the right technology to lead to accurate data to deliver better patient care and improve outcomes. Feel free to share but we would appreciate a Health Catalyst citation.


Avoiding Carsickness When the Cars Drive Themselves

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The day is approaching when commuters stuck in soul-crushing traffic will be freed from the drudgery of driving. Companies are investing billions to devise sensors and algorithms so motorists can turn our attention to where we like it these days: our phones. But before the great promise of multitasking on the road can be realized, we need to overcome an age-old problem: motion sickness. "The autonomous-vehicle community understands this is a real problem it has to deal with," said Monica Jones, a transportation researcher at the University of Michigan. "That motivates me to be very systematic."