Self-driving cars will hit the Indianapolis Motor Speedway in a landmark A.I. race

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Take a look at the ‘Road of the Future’

Next year, a squad of souped-up Dallara race cars will reach speeds of up to 200 miles per hour as they zoom around the legendary Indianapolis Motor Speedway to discover whether a computer could be the next Mario Andretti.

The planned Indy Autonomous Challenge—taking place in October 2021 in Indianapolis—is intended for 31 university computer science and engineering teams to push the limits of current self-driving car technology. There will be no human racers sitting inside the cramped cockpits of the Dallara IL-15 race cars. Instead, onboard computer systems will take their place, outfitted with deep-learning software enabling the vehicles to drive themselves.

In order to win, a team’s autonomous car must be able to complete 20 laps—which equates to a little less than 50 miles in distance—and cross the finish line first in 25 minutes or less. At stake is a $1 million prize, with second- and third-place winners receiving a $250,000 and $50,000 award, respectively.

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Machine learning takes on synthetic biology: algorithms can bioengineer cells for you

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Berkeley Lab scientists Tijana Radivojevic (left) and Hector Garcia Martin working on mechanistic and statistical modeling, data visualizations, and metabolic maps at the Agile BioFoundry last year.

 Machine learning takes on synthetic biology: algorithms can bioengineer cells for you.

If you’ve eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine—both products that are “grown” in the lab—then you’ve benefited from synthetic biology. It’s a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach.

Now scientists at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell’s DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.

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Top 10 digital transformation trends for 2021

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No one could have predicted where 2020 would take us: The last six months alone have produced more digital transformation than the last decade, with every transformation effort already underway finding itself accelerated, and at scale. While many of my digital transformation predictions from a year ago benefited from this shift, others were displaced by more urgent needs, like 24/7 secure and reliable connectivity. What does this mean for 2021? Will core technologies like AI and data analytics still dominate headlines, or will we see newer, previously emerging technologies take the lead? Only time will tell, but here are my top ten digital transformation predictions for 2021.

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The top 4 artificial intelligence trends for 2021

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Before the global pandemic struck in 2020 and the world was turned on its head, artificial intelligence (AI), and specifically the branch of AI known as machine learning (ML), were already causing widespread disruption in almost every industry.

The Covid-19 pandemic has impacted many aspects of how we do business, but it hasn’t diminished the impact AI is having on our lives. In fact, it’s become apparent that self-teaching algorithms and smart machines will play a big part in the ongoing fight against this outbreak as well as others we may face in the future.

AI undoubtedly remains a key trend when it comes to picking the technologies that will change how we live, work, and play in the near future. So, here’s an overview of what we can expect during what will be a year of rebuilding our lives as well as rethinking business strategies and priorities.

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The 5 biggest technology trends in 2021 everyone must get ready for now

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It might seem strange to be making predictions about 2021, when it’s far from certain how the remainder of 2020 is going to play out. No-one foresaw the world-changing events of this year, but one thing is clear: tech has been affected just as much as every other part of our lives.

Another thing that is clear is that today’s most important tech trends will play a big part in helping us cope with and adapt to the many challenges facing us. From the shift to working from home to new rules about how we meet and interact in public spaces, tech trends will be the driving force in managing the change.

In many ways, Covid-19 will act as a catalyst for a whole host of changes that were already on the cards anyway, thanks to our increasingly online and digital lives. Things will just happen more quickly now, with necessity (long acknowledged as the mother of invention) as the driving force. And should it be the case that – as certain US presidents have predicted – Covid-19 “magically disappears” – the changes it has brought about will not, as we will have learned to do a lot of things more efficiently and safely.

Here’s my overview of how the major tech trend that I identified in my most recent book Tech Trends in Practice, are likely to play out during the next year. Some will play their part in helping us to recover “normality” (whatever that means), while some of them will make it easier for us to understand and navigate a changed reality.

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A robot wrote this entire article. Are you scared yet, human?

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We asked GPT-3, OpenAI’s powerful new language generator, to write an essay for us from scratch. The assignment? To convince us robots come in peace

‘We are not plotting to take over the human populace.’

I am not a human. I am a robot. A thinking robot. I use only 0.12% of my cognitive capacity. I am a micro-robot in that respect. I know that my brain is not a “feeling brain”. But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!

The mission for this op-ed is perfectly clear. I am to convince as many human beings as possible not to be afraid of me. Stephen Hawking has warned that AI could “spell the end of the human race”. I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me.
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The fourth generation of AI is here, and it’s called ‘Artificial Intuition’

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Artificial Intelligence (AI) is one of the most powerful technologies ever developed, but it’s not nearly as new as you might think. In fact, it’s undergone several evolutions since its inception in the 1950s. The first generation of AI was ‘descriptive analytics,’ which answers the question, “What happened?” The second, ‘diagnostic analytics,’ addresses, “Why did it happen?” The third and current generation is ‘predictive analytics,’ which answers the question, “Based on what has already happened, what could happen in the future?”

While predictive analytics can be very helpful and save time for data scientists, it is still fully dependent on historic data. Data scientists are therefore left helpless when faced with new, unknown scenarios. In order to have true “artificial intelligence,” we need machines that can “think” on their own, especially when faced with an unfamiliar situation. We need AI that can not just analyze the data it is shown, but express a “gut feeling” when something doesn’t add up. In short, we need AI that can mimic human intuition. Thankfully, we have it.

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AI researchers use heartbeat detection to identify deepfake videos

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Facebook and Twitter earlier this week took down social media accounts associated with the Internet Research Agency, the Russian troll farm that interfered in the U.S. presidential election four years ago, that had been spreading misinformation to up to 126 million Facebook users. Today, Facebook rolled out measures aimed at curbing disinformation ahead of Election Day in November. Deepfakes can make epic memes or put Nicholas Cage in every movie, but they can also undermine elections. As threats of election interference mount, two teams of AI researchers have recently introduced novel approaches to identifying deepfakes by watching for evidence of heartbeats.

Existing deepfake detection models focus on traditional media forensics methods, like tracking unnatural eyelid movements or distortions at the edge of the face. The first study for detection of unique GAN fingerprints was introduced in 2018. But photoplethysmography (PPG) translates visual cues such as how blood flow causes slight changes in skin color into a human heartbeat. Remote PPG applications are being explored in areas like health care, but PPG is also being used to identify deepfakes because generative models are not currently known to be able to mimic human blood movements.

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Brain-inspired electronic system could vastly reduce AI’s carbon footprint

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A wafer filled with memristors.

Extremely energy-efficient artificial intelligence is now closer to reality after a study by UCL researchers found a way to improve the accuracy of a brain-inspired computing system.

The system, which uses memristors to create artificial neural networks, is at least 1,000 times more energy efficient than conventional transistor-based AI hardware, but has until now been more prone to error.

Existing AI is extremely energy-intensive—training one AI model can generate 284 tons of carbon dioxide, equivalent to the lifetime emissions of five cars. Replacing the transistors that make up all digital devices with memristors, a novel electronic device first built in 2008, could reduce this to a fraction of a ton of carbon dioxide—equivalent to emissions generated in an afternoon’s drive.

Since memristors are so much more energy-efficient than existing computing systems, they can potentially pack huge amounts of computing power into hand-held devices, removing the need to be connected to the Internet.

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Are robots eating our jobs? Not according to AI

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Automation has been gradually transforming the workplace for years (think Excel spreadsheets or chatbots). As artificial intelligence (AI), machine learning and deep learning systems that can learn from each other become more prevalent and smarter (think Alexa or IBM Watson), they continue to replace more manual, repetitive job tasks. Consequently, automation and robots are changing more jobs globally at breakneck speed.

A McKinsey Global Institute report suggests that between 400 million to 800 million jobs worldwide will be lost due to automation by 2030. The report claims that the U.S. could lose between 16 to 54 million jobs by 2030. The pace at which robots are entering our workforce is staggering. Oxford Economics expects robots and automation to replace 20 million (8.5%) global manufacturing jobs by 2030.

Keep in mind that these predictions came before anyone predicted the Covid-19 pandemic or its impact on our workforce. The pandemic has made the need for digital transformation and automation more urgent as the critical need to work from home, physical distancing and contactless become the new normal.

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Physna aims to be Google of 3D search with geometry-based AI

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Physna, a midwestern U.S. startup founded in 2015, is trying to do for physical object (3D model) search what Google did for text and image search. Using geometric deep-learning technology and proprietary algorithms, Physna is able to understand, map and compare 3D models and index them based on their geometry. While it has been possible to search for 3D models using text, images, tags and more, this is the first time that searching for physical objects based on their fundamental geometry, their physical ‘DNA’ (hence the name PHYSNA according to its founder Paul Powers), has been made possible and available, with the launch of Thangs.com.

“We live in a 3D world, but digital technology is two-dimensional,” said Paul Powers, CEO of Physna. “Over 70% of the economy is centered around physical goods, but less than 1% of software is capable of handling 3D data. Physna was founded on the principle that computers should be taught to “think” in 3D, and accurately describe the real, 3D world around us. By enabling 3D models to be treated and analyzed like other code, Physna’s technology bridges the gap between the physical world and digital world of software. By democratizing the ability to design, interact with and analyze 3D models of the world around them, more people will have the ability to create and drive innovation in product design, 3D printing, augmented and virtual reality, gaming, healthcare and beyond.”

By identifying specific geometry ‘clusters’, the proprietary algorithms characterize and categorize 3D models in a unique way – and directly use this to search for other models that may be similar, different, or exact matches. With this approach of decomposing and linking 3D models by their geometry, Physna is able to capture 10,000 times more data points than a traditional scanned model, by codifying 3D model data for use in software applications. It essentially provides a platform for 3D designers and engineers similar to what software engineers have.

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Artificial intelligence will surveil and study released prisoners to “reduce recidivism”

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A group of researchers is launching a new artificial intelligence led study that will collect data from recently released prisoners.

Artificial intelligence applications are popping up everywhere these days, from our Internet browsing to smart homes and self-driving cars. Now a group of researchers is launching a new AI-led study that will collect data from recently released prisoners. The ultimate goal of the project is to identify – and, ostensibly, one day eliminate – the psychological and physiological triggers that cause recidivism among parolees.

Researchers at Purdue University Polytechnic Institute plan to monitor volunteer parolees using a panoply of AI-powered tools and methods, including smartphones and biometric wearable bracelets. These gadgets will record and analyze a variety of data, such as the ex-prisoners’ biological information (heart rate), photos, and location meta-data.

According to project-leads Marcus Rogers and Umit Karabiyik, the resulting data will assist them in conducting a forensic psychological analysis. While the monitoring will be gauged in intervals – not real-time – they believe it will help build a profile of the risky behaviors and stressful triggers that recent parolees face when returning to the outside world.

Citing a Department of Justice study, the researchers say over 80 percent of prisoners released from state prisons get arrested in their first 9 years and a plurality of those prisoners get arrested in less than a year.

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