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If fiction is to believe, we should be wary of artificial intelligence. Even the world’s biggest innovators believe that AI is a beast that we shouldn’t poke too much with a stick: Elon Musk calls the technology, “summoning the demon”.
Dementia is one particular field that doctors have struggled to find a cure for. Given how little is known about the brain – and given that dementia is difficult even to diagnose in some patients, let alone treat – the disease is a scientific grey area in more ways than one. Thanks to artificial intelligence however, technology is helping to revolutionise the way that we fight this debilitating condition.
Diagnosis of dementia starts early: in fact, there are traceable changes made in the brain up to two decades before the noticeable signs of the disease become apparent. Science is helping to develop computer programs in order to notice these signs. Perhaps most impressively, AI has been known to detect Alzheimer’s from brain scans from an average of six years before a subject’s diagnosis confirming the condition.
Machine learning principally relies on data and statistical algorithms. Supposing that data scientists can ascertain huge datasets that contain the information of many sufferers of dementia, the algorithms can notice patterns and similarities between them. The idea with machine learning is that the AI learns for itself, producing results without being told to. in the case of diagnosis, machine learning can analyse differences between the physical structure of different brains in deeply detailed scans. The algorithms are capable of seeing chemical changes that can’t otherwise be spotted by doctors.
So if you can see the indications of such a horrific condition, surely you can predict the patterns of the suffers and eliminate future cases?
Either way, there are plenty of ways that predicting how a brain could mature can aid a sufferer of dementia. Brain conditions might not always be treatable: despite all the progress that science is making in being able to spot the signs and eradicate diseases, it’s not certain that a definitive cure of Alzheimer’s or dementia could be found in the next few years. The care and preparation that goes into looking after those afflicted with the condition are key though; if doctors can at least notice when a subject is showing signs, then at the very least, a sudden and shocking diagnosis can be softened, like living arrangements, care packages and potential treatment can be planned.
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It’s not just Alzheimer’s, dementia and related brain conditions that machine learning can help to diagnose and treat. AI is a big tool for neurologists to explore, thanks to research in artificial neural networks (ANN) that act in a similar way to a biological brain, however AI is also being used in other medical fields.
Cancer is still one of the most lethal diseases worldwide and one that claims the lives of millions every year. With millions being pumped into looking for cures and treatments, there is a lot of research and investment currently in artificial intelligence. In a similar way to dementia, huge datasets of patients can be analysed to diagnose the signs of cancer even sooner and provide immediate attention. Equally, the treatment offered to patients is improving thanks to machine learning too.
Artificial intelligence is of course, something that we should approach with caution. But whilst AI can have us question our own humanity, it may end up helping us to connect with it in ways deeper than we ever expected. Throughout human history, technology has helped to save lives and progress medical science: why should AI be any different? The future is bright when it comes to looking for a cure for dementia. In a few years’ time, as the technology evolves, perhaps it will open doors to find more treatments for other diseases too.Mark White
Mark White is the editor of Top Business Tech, a title that focuses on AI, IoT, blockchain and emergent technology.
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Phone use while driving is still a huge problem, but this is helping
Apple’s Do Not Disturb feature appears to be having a positive affect on driver safety, but the lure of the smartphone is still leading to plenty of dangerous driving a new study has concluded. While speeding remains the number one unsafe driving behavior, phone use while at the wheel is right behind it.
Indeed, 38-percent of trips across the US involve drivers speeding, while 37-percent see drivers use their phone in some way while they ought to be paying attention to the road. The new figures come from EverQuote, maker of the EverDrive app. It uses a phone’s GPS and other sensors to track how aggressively drivers corner, accelerate, and brake, as well as what other activities they do, while on the road.
It’s been downloaded enough for EverQuote to have 781 million miles worth of data to play with, in fact. From that, the company says that on average 6-percent of time on trips are spent on the phone; in trips recorded with unsafe driving, more than a third of them involved phone use. It’s no small issue, either, with 1,000 accidents each day believed to be caused by distracted driving, according to the CDC.
One thing that can help, despite initial skepticism, is Do Not Disturb While Driving. Launched by Apple on the iPhone back in September 2023, it prevents notifications like text messages and calls from sounding while the vehicle is in motion. Only those contacts who have been whitelisted are allowed to break through the block.
According to EverQuote, 70-percent of iPhone users it looked at kept the Do Not Disturb While Driving features enabled after the iOS 11 update. For the month or so following its release, those users were on their iPhone 8-percent less. Not a huge amount, perhaps, but considering the amount of phone use while at the wheel overall, anything is clearly better than nothing.
Android, meanwhile, has added its own Do Not Disturb mode for driving use. Launched on the Pixel 2 and Pixel 2 XL, it also uses sensor data to decide when the phone is in a moving vehicle, and trim the number of alerts and notifications that are permitted during that time. Rather than needing to be manually activated at the start of each journey, the Android system figures out intelligently whether or not to allow notifications. This year, there’ll be an API for third-party developers and device-makers to use, to integrate the same features into their own apps and phones.
75-percent of people surveyed by EverQuote said that they felt the Do Not Disturb features made them safer drivers. Still, the EverDrive app suggests there’s plenty of work still to be done, particularly among younger users. Those aged 18-20 use their phones on almost half of all their trips, it was found; drivers 21 or over use them on 38-percent of trips.
What if we wake up one morning to the news that a super-power AI has emerged with disastrous consequences? Nick Bostrom’s Superintelligent and Max Tegmark’s Life 3.0 books argue that malevolent superintelligence is an existential risk for humanity. Rather than endless anticipation, it’s better to ask a more concrete, empirical question: What would alarm us that superintelligence is indeed at the doorstep? If an AI program develops fundamental new capabilities, that’s the equivalent of a canary collapsing. AI’s performance in games like Go, poker, or Quake 3, is not a canary. The bulk of AI in such games is social work to highlight the problem and design the solution. The credit for AlphaGo’s victory over human Go champions was the talented human team at DeepMind that merely ran the algorithm the people had created. It explains why it takes several years of hard work to translate AI success from one little challenge to the next. Techniques such as deep learning are general, but their impactful application to a particular task needs extensive human intervention. Over the past decades, AI’s core success is machine learning, yet the term ‘machine learning’ is a misnomer. Machines own only a narrow silver of humans’ versatile learning abilities. If you say machine learning is like baby penguins, know how to fish. The reality is that adult penguins swim, catch fish, digest it. They regurgitate fish into their beaks and place morsels into their children’s mouths. Similarly, human scientists and engineers are spoon-feeding AI. In contrast to machine learning , human learning plans personal motivation to a strategic learning plan. For example, I want to drive to be independent of my parents (Personal motivation) to take driver’s ed and practice on weekends (strategic learning). An individual formulates specific learning targets, collects, and labels data. Machines cannot even remotely replicate any of these human abilities. Machines can perform like superhuman; including statistical calculations, but that is merely the last mile of learning. The automated formula of learning problems is our first canary, and it does not seem anywhere close to dying. The second canary is self-driving cars. As Elon Musk speculated, these are the future. Artificial intelligence can fail catastrophically in atypical circumstances, like when an individual in a wheelchair crosses the street. In this case, driving is more challenging than any other AI task because it requires making life-critical, real-time decisions based on the unpredictable physical world and interaction with pedestrians, human drivers, and others. We should deploy a limited number of self-driving cars when they reduce accident rates. Human-level driving is achieved only when this canary be said to have kneeled over. Artificial intelligence doctors are the third canary. AI already has the capability of analysing medical images with superhuman accuracy, which is a little slice of a human doctor’s job. An AI doctor’s responsibility would be interviewing patients, considering complications, consulting other doctors, and so on. These are challenging tasks, which require understanding people, language, and medicine . This type of doctor would not have to fool a patient into wondering it is human. That’s why it is different from the Turing test. A human doctor can do a wide range of tasks in unanticipated situations. One of the world’s most prominent AI experts, Andrew Ng, has stated, “Worrying about AI turning evil is a little bit like worrying about overpopulation on Mars.”
Helping Creativity Flow Alt-comics legend Lynda Barry gives CGS Stone Lecture tomorrow
Ladies and gentleman, giving this year’s Stanley P. Stone Distinguished Lecture…it’s Professor Skeletor!
OK, not really. It’s actually alternative comics great Lynda Barry, creator of the late, lamented weekly strip Ernie Pook’s Comeek. Also the author of several graphic novels, Barry has reinvented herself as a creativity guru—although she’d hate the g-word—helping would-be artists break through self-imposed barriers to find creative fulfillment. In the Making Comics class she teaches at the University of Wisconsin–Madison, she starts the process by having her students adopt classroom aliases.
“We have Cactus and Goodfella and Hoops, Sister Mary Ignatius—who’s a dude—and I’m Professor Skeletor,” Barry says.
It’s a little trick to take away students’ self-consciousness, one of several ways she tries to free up their hands for drawing. She also encourages physically stretching their hands, timed no-stopping drawing exercises, and assignments like “draw yourself as Batman.”
Barry comes to BU to deliver the annual Stone Lecture tomorrow, November 9, at Sleeper Auditorium, in an event also supported by Kilachand Honors College. The lecture series brings notable speakers to the College of General Studies. While on campus, she will also visit the Serious Comics course taught by Davida Pines, a CGS associate professor and chair of the division of rhetoric, who suggested her for the lecture.
“In college, I had a teacher who asked me this question: what is an image?” Barry says. “I have a lot of different answers to what it is, and its function, and it started me thinking about what the biological function of this thing we call the arts might be.”
She followed her curiosity to academia, becoming a UW associate professor of interdisciplinary creativity and offering weeklong creativity workshops around the country. “I’d say only about a quarter of the students feel really comfortable drawing, and maybe a third of them don’t feel terrified,” she says. “Two thirds feel terrified and want to start crying, and those are my favorite people to work with.”
That’s because their drawing style is unchanged from the time they quit, she says—usually at the age of 8 or 10, when they realized they couldn’t draw a nose or hands and gave up. “‘That’s it, it’s all over for me, I’m washed up.’ But that charming style that little kids have, that’s still intact,” Barry says. “And for those people, their trajectory to being able to make comics is so much faster than the trajectory of somebody who has a lot of experience doing beautifully drawn work.
“Think about it. You’d never want to see Charlie Brown with a hyper-realistically drawn nose—it would be horrifying. That little sideways parentheses is enough.”
The makeup of Barry’s classes at Wisconsin ranges from freshmen to PhD students, and she also works with a group of four-year-olds in a university preschool every week.
“When I came to the university,” she says, “one thing that struck me was how miserable the grad students were. I thought, I wonder if I could pair them up with four-year-olds?” She started a program called Draw Bridge that did just that. “What I hoped would happen was my students would learn to borrow the kids’ state of mind and learn to approach problems in a way that was less tight and focused, a way that was happier and set the conditions for discovery.”
Having written several acclaimed graphic novels, Barry in 2008 turned her hand to books on the creative process. She has written three: Syllabus: Notes from an Accidental Professor, What It Is, (about writing) and Picture This (about art).
Pines took one of Barry’s workshops this past summer, and she now inserts favorite quotes from Syllabus in her own syllabi: Any story we write or picture we make cannot demonstrate its worth until we write or draw it. The physical act of writing or drawing is what brings the inspiration about. Worrying about its worth and value to others before it exists can keep us immobilized forever.
“I am someone who stops myself incessantly and edits avidly, and it’s very difficult to get stuff on the page before I’ve already decided that it’s not going to be good enough,” Pines says. “As a composition teacher, I try to help my students not become me.”
Pines says some students groan when she calls for a two-minute self-portrait, but “you don’t have time to think, what’s the right shape for my head? It’s a playful way of letting go and getting your hand moving,” she says. When the exercise is over, “my students are looser, they’re happier, and they’re not so sure that they can’t do it.”
Lynda Barry will speak on Creativity: What It Is at the Stanley P. Stone Distinguished Lecture on Thursday, November 9, at 5 p.m., in CGS Jacob Sleeper Auditorium, 871 Commonwealth Ave. The event is free and open to the public. Seating is first come, first served.
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The open source PostgreSQL project is out with a major new database release this week adding in support for features that expand scalability, security and performance.
PostgreSQL 9.0 includes new features that developers have been asking about for years including replication and hot standby capabilities. The PostgreSQL 9.0 release continues the evolution of the 14 year old open source project for the new era of virtualization and cloud computing.
“If you asked PostgreSQL users three years ago what were their top ten list of missing features, built-in easy replication and in-place upgrades would have been on every single user’s list,” Josh Berkus, PostgreSQL core team member, told chúng tôi “Now, with 9.0, we have both of those things. Also, we’ve added some other features which require more than the usual amount of testing for upgrades, particularly regarding the stored procedure language PL/pgSQL. Having the “double 0″ number (9.0.0) tells our users that.”
The PostgreSQL 9.0 release is the first in the 9.x branch and follows the PostgreSQL 8.4 release which debuted in July of 2009.
Berkus explained that hot standby and streaming replication are two complimentary features which can be used either together or separately. Previous versions of PostgreSQL had a feature called ‘warm standby’ which enabled PostgreSQL users to have a dormant failover server by copying binary log files from the master to the standby.
“Hot standby improves this by allowing you to run read-only queries against the standby server, making it useful for reporting and load balancing,” Berkus said.
Hot Standby however is an asynchronous type of replication which could potentially be hours behind the master database.
“This is where streaming replication comes in; it opens a database port connection between the master and the standby and ships over the data changes as they are synced on the master,” Berkus said. “This means the standby can be as little as a few milliseconds behind.”
Berkus said the tradeoff between streaming replication and hot standby is all about the load on the master database. With hot standby there isn’t much load on the master, whereas streaming replication could add extra load.
“As part of testing the new replication, I benchmarked having six standby servers off of one master on Amazon EC2 and the resource utilization on the master was less than 10 percent higher than no standbys at all,” Berkus said. “The incremental cost of adding new standbys is extremely low, which means that you can have a lot of them for ‘bursty’ traffic.”
Berkus also noted that the new replication features in PostgreSQL 9.0 require much lower administration than older replication tools, allowing a single sysadmin or lead developer to manage a large cluster of replicated servers without needing a full-time database expert.
“For a cloud host, which might have hundreds of PostgreSQL nodes, that’s essential,” Berkus. “In addition to the replication, the security features in 9.0 are critical to multi-tenant environments, as well as the query planner improvements to make object-relational mapper queries execute better.”PostgreSQL 9.1
With PostgreSQL 9.0 now out the door, planning on the features for the next release are already underway.
“We have a whole slate of improvements to replication lined up for 9.1, the biggest of which is adding synchronous options,” Berkus said. “We’ve also got teams working on enhanced security options, using label-based access control, which should make multi-tenanting more secure. And there are several clustering projects, like PostgresXC, which potentially could allow multi-master scaling when they reach production quality.”
There is also a new technology under development called SQL/MED, which is a protocol for federating databases. VoIP vendor Skype introduced technology for PostgreSQL in 2006 called PL/Proxy for data partitioning that could serve as a starting point.
“Skype showed us all how one could use federated databases through PL/proxy to scale to 200 server nodes,” Berkus said. “With SQL/MED, PostgreSQL users will be able to do this even with some of the nodes being other database systems. That feature may take more than one year to mature, however.”
Sean Michael Kerner is a senior editor at chúng tôi the news service of chúng tôi the network for technology professionals.
The world has seen unprecedented technological change over the past few decades, impacting every aspect of human life. One area where it is expected to have far-reaching implications is military strategy and weapons manufacture. Artificial Intelligence (AI) and Autonomy are transforming how nations wage war, and their impact will be profound.
AI-enabled systems can revolutionize where and how wars are fought. Small, cheap, and increasingly capable uncrewed systems will replace large, expensive, crewed weapon platforms. This revolution is already underway in many parts of the world. For example, Ukraine has developed sophisticated armed drones that strike with precision, while Russia is using AI “smart” mines that respond to nearby footsteps. Australia has a range of autonomous weapons and vessels that can be deployed in conflict, including uncrewed Ghost Bat aircraft and Bluebottle surveillance vessels.
Major powers around the world recognize the importance of AI in shaping the future of warfare. The House of Lords in the UK is holding a public inquiry to study the use of AI in weapons systems, while Luxembourg recently hosted an important conference on autonomous weapons. The United States has adopted a “third offset strategy” that will invest heavily in AI, autonomy, and robotics. Meanwhile, China has already announced its intention to become the world leader in AI by 2030.
In this article, we will examine how AI and autonomy fit into the larger strategic picture and why it is crucial for countries to incorporate them into their defense strategy.AI and Autonomy: The Future of Warfare
AI and autonomy will change the way wars are fought in different ways. Autonomous systems can function independently or with minimal human intervention, allowing militaries to operate unmanned aerial vehicles (UAVs), ground robots, and uncrewed ships. In contrast, artificially intelligent systems can help decision-makers analyze vast amounts of data generated by sensors and other sources to provide a more accurate and timely picture of the battlefield.
The use of autonomous systems in warfare is not new. Drones have been used extensively in Iraq and Afghanistan, while underwater drones have been used by navies worldwide for years. However, the increasing sophistication of AI-enabled systems is expected to revolutionize this field. These systems can act faster than human decision-makers, react with greater accuracy, and adapt to changing circumstances in real time.The Role of AI and Autonomy in Military Strategy
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One area where AI has shown its value is in identifying targets in satellite images. In 2014, the US Air Force demonstrated how machine learning algorithms could identify a T-90 main battle tank in a satellite image with an accuracy rate of 91%. Another area is facial recognition technology, which can help military personnel identify high-value targets in a crowd accurately. Additionally, AI-powered text generation can help create information operations to influence public opinion or deceive enemy forces.AI and Autonomy: The Risks and Challenges
Despite the potential benefits of AI and autonomy in military strategy, there are risks and challenges we must address. One significant concern is the possibility of an arms race involving autonomous weapons. As AI specialist Steve Omohundro warned in 2014, “An autonomous weapons arms race is already taking place.”
The proliferation of autonomous weapons could lead to an escalation of violence as countries race to develop ever more sophisticated systems. There is also the risk of autonomous weapons malfunctioning or being hacked, thereby leading to unintended consequences.
Another challenge is the ethical concerns surrounding autonomous weapons. The use of artificial intelligence in warfare raises several questions about accountability and responsibility. For example, who is accountable if an autonomous weapon causes collateral damage or malfunctions? How do we ensure that these systems are used ethically and comply with international law?
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AI and autonomy are transforming military strategy at an unprecedented pace, and their impact will be profound. Countries worldwide recognize the importance of artificial intelligence in shaping the future of warfare. They are hence investing heavily in research and development to stay ahead of the curve.
However, there are risks and challenges involved in the use of AI in designing and deploying war weapons. The proliferation of autonomous weapons could lead to an escalation of violence, and there are concerns about accountability and responsibility. Therefore, it is essential for countries to incorporate ethical considerations into their decision-making around AI-enabled systems.
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