Connect with us

Technology

Goodbye silicone? A new era of breast reconstruction is on the horizon | Breast cancer

Voice Of EU

Published

on

Having an ice pack strapped to your chest – that’s how some describe the experience of taking a walk in cold weather when you have breast implants. Silicone only slowly reaches body temperature once out of the cold, so that icy feeling can persist for hours. As well as being uncomfortable, for breast cancer survivors it can be an unwelcome reminder of a disease they would rather put behind them.

Every year, 2 million people worldwide are diagnosed with breast cancer and the treatment often involves removing at least one breast. But most choose not to have their breasts reconstructed; in the UK, it is only about 30%. Now a handful of startups want to change that, armed with 3D-printed implants that grow new breast tissue before breaking down without a trace. “The whole implant is fully degradable,” says Julien Payen, CEO of the startup Lattice Medical, “so after 18 months you don’t have any product in your body.”

It could spell the end not only of cold breasts, but the high complication rates and long surgeries associated with conventional breast reconstruction. The first human trial of such an implant, Lattice Medical’s Mattisse implant, is scheduled to begin on 11 July in Georgia. Others will soon follow. “We expect to start clinical trials in two years’ time,” says Sophie Brac de la Perrière, CEO of another startup, Healshape.

“It’s exciting,” says Stephanie Willerth, professor of biomedical engineering at the University of Victoria, Canada, who is not involved with the companies. “As engineers, we’ve been playing with 3D printing for half a decade”, but having a clinical use that doctors recognise as useful for patients is key to getting the technology out there, she says.

But in a field fraught with difficult medical compromises, unequal access issues and expectations about what women want, the question is how big an impact the new technology will actually have.


Today, there are two main types of breast reconstruction: silicone implants and flap surgery. While implants are easy to install, flap surgery is a highly specialised business that requires a tissue “flap” being taken from the stomach, thigh or back. Surgeons often recommend flaps because, while there’s a lot of initial surgery and a longer recovery period, it gives a good, long-lasting result.

Silicone is still the most common choice. It is easy and simple, which appeals to cancer patients who either medically can’t have or mentally can’t face having tissue removed from another part of their body. But “it’s far from perfect”, says Shelley Potter, an oncoplastic surgeon at the University of Bristol and the Bristol Breast Care Centre. “It’s quite high risk. There’s a 10% chance of losing an implant.”

Healshape’s 3D-printed hydrogel implant
Healshape’s 3D-printed hydrogel implant, designed to be colonised by the patient’s fat cells over six to nine months. The company hopes to start trials in two years’ time. Photograph: Healshape

Silicone implants also require replacement every 10 or so years and they have had their fair share of scandals: the 2010s PIP scandal, in which a major implant manufacturer was found to have made its implants of dodgy silicone, and the 2018 Allergan scandal, in which popular textured implants were linked to an increased risk of a rare lymphoma. And as an American study from last year shows, it is mainly the idea of having that foreign object stuck inside your body that puts many off reconstruction altogether.

“So what we want to do,” says Brac de la Perrière, “is to give the benefits of the different solutions without the constraints.” In other words: the single, simple surgery of an implant, but without any lingering foreign material to cause trouble.

This can be achieved in different ways. Healshape uses a hydrogel to 3D-print a soft implant that will slowly be colonised by the person’s own fat cells, the initial batch of which is injected, while the implant disappears over six to nine months. The company CollPlant is developing something similar using a special collagen bioink, extracted from tobacco leaves it has genetically engineered to produce human collagen. “I think it will change the opinion of many patients,” says CEO, Yehiel Tal.

Lattice Medical has a different approach. Its implant is a 3D-printed cage made of a degradable biopolymer, in which they encase a small flap from underneath the breast area. This flap then grows to fill the cage with fat tissue, while the cage itself is absorbed by the body, ultimately leaving a regrown breast in its place.

Lattice Medical’s Mattisse implant
Lattice Medical’s Mattisse implant. Vascular adipose tissue is inserted into a bio-resorbable ‘tissue engineering chamber’, which degrades over 18 months. Trials are imminent. Photograph: Lattice Medical

Regrowing breasts using a cage has been shown to work in humans before, in a 2016 trial. However, it only worked in one of five women and the cages were not degradable. Andrea O’Connor from the University of Melbourne, Australia, who led the trial’s engineering team, hopes the new trial will address the problems raised in the first – for example, that patient responses can vary greatly. But if successful, it “would have the potential to help many women to achieve a superior reconstruction”, she says. Lattice Medical says its cage is an improvement because a flat base and larger pores help the tissue grow.

One big unknown is how much feeling the regrown breasts will have. A mastectomy usually means losing some sensation and, according to plastic surgeon Stefania Tuinder from the Maastricht University Medical Centre+ in the Netherlands, reconstruction affects it too. “From our data, it seems that implants have a negative effect on sensation, so the feeling in the skin is less than when you have only a mastectomy,” she says. In comparison, reconstruction from a flap with connected nerves can bring back some feeling within a few years.

Tuinder suspects the implant numbness is both because of nerve damage when the implants are inserted, and because the nerves can’t grow back once they are blocked by a lump of silicone. Whether that will also apply to the new implants remains to be seen, but since eventually there will be nothing to block the nerves, hopes are that sensation will be better.


Tissue engineered implants, however, are not the only recent innovations in the field. Many groups are working on perfecting a reconstruction technique using injections of the person’s own fat, boosted with extra stem cells to help the tissue survive. Medical professionals are still debating the safety and how the breasts hold up long term. In contrast to the new implants, the procedure might have to be done several times.

While any of these new techniques could result in something better than what’s currently on offer, Potter warns that we have a tendency to jump at new and shiny tech – an optimism bias. “We always think it’s going to be brilliant,” she says, but “we don’t want a situation like with vaginal mesh, where in 10 years’ time … we find out we have done something that isn’t helpful.”

Other solutions to the problems of reconstruction do exist. One is living without breasts, known as “going flat”. Contrary to the companies that think they can turn the reconstruction statistics around, people within the flat movement argue that if people were better informed, even more would opt out. “I reckon if [going flat] was given as an equal option,” says Gilly Cant, founder of the charity Flat Friends, “at least another 30-50% of women wouldn’t have [reconstruction].”

A Healshape scientist using software to determine the shape of an implant prior to 3D printing. The implants can be custom-made to suit the patient.
A Healshape scientist using software to determine the shape of an implant prior to 3D printing. The implants can be custom-made to suit the patient. Photograph: Healshape

At the moment, the guidance from the National Institute for Health and Care Excellence (Nice) says that doctors should be aware that some might not want reconstruction. But Cant says it is often presented to people as part of the treatment process. “It’s like, ‘OK, we need to do a mastectomy. Then you have chemo. Then you’ll have your radiotherapy and then we’ll do reconstruction.’ So women live for that reconstruction at the end,” she says. It comes to signal the finish line.

It is particularly contentious when only one breast is removed, because some might want the other taken off to feel and look symmetrical, rather than have a new one made. But according to Cant, many doctors don’t want to remove a healthy breast. Part of the doctors’ concern is that women will regret their decision, says Potter, but “women know what they want to do with their own bodies. We should help and support them to do what they want to do.”

Potter herself would like to see more of the ultimate alternative: not having a mastectomy in the first place. “There’s no evidence that mastectomy gives you better cancer outcomes than a breast-conserving operation,” she says. In this case, the tumour is removed but the breast is kept. For example, one of her patients had a breast reduction that removed her cancer while giving her breasts a lift. “She calls them her silver lining breasts.”


So even without tissue-engineered implants, there are enough options to make the choice a hard one. To help people choose, some charities pair up people considering a specific procedure with someone who has already been through it. At the charity Keeping Abreast, show and tell sessions give people the chance to ask the questions they might be uncomfortable asking their doctor and see the results for themselves.

But according to a 2018 report by the all-party parliamentary group on breast cancer, knowing what you want is not the same as having access to it. “There’s a massive postcode lottery,” says Potter. It stems from flap surgery being so involved that it often requires specialist plastic surgeons who can do minute surgery under a microscope. Many clinics don’t have such experts in-house and while the Nice guidance says people should still have the option, in practice it limits access.

The companies say this won’t be a problem with the new implants, because they are specifically designed to be easy to put in. Flap surgery can take from three to 12 hours depending on the flap, but insertion of Lattice Medical’s implant, for example, takes only one hour and 15 minutes. “It’s really accessible to all plastic surgeons,” says Payen.

This accessibility will no doubt be crucial in taking the new implants from a cool technology to something with real impact. But from Potter’s perspective, it’s just one potential piece in a big puzzle, not a techno-fix. The implants “would be an option for a lot of women”, she says. “But I think the main advance is all around access, proper information, giving women choice and hopefully reducing the number of mastectomies that we need.”

Source link

Technology

India’s latest rocket flies but payloads don’t prosper • The Register

Voice Of EU

Published

on

India’s small satellite launch vehicle (SSLV) made a spectacular debut launch on Sunday, but the mission fell short of overall success when two satellites were inserted into the incorrect orbit, rendering them space junk.

The SSLV was developed to carry payloads of up to 500 kg to low earth orbits on an “on-demand basis”. India hopes the craft will let its space agency target commercial launches.

Although it is capable of achieving 500 km orbits, SSLV’s Saunday payload was an 135 kg earth observation satellite called EOS-2 and student-designed 8 kg 8U cubesat AzaadiSAT. Both were intended for a 356 km orbit at an inclination of about 37 degrees.

That rocket missed that target.

Indian Space Research Organisation (ISRO) identified the root cause of the failure Sunday night: a failure of logic to identify a sensor failure during the rocket stage.

ISRO further tweeted a committee would analyse the situation and provide recommendations as the org prepared for SSLV-D2.

ISRO Chairman S Somanath further explained the scenario in a video statement, before vowing to become completely successful in the second development flight of SSLV. “The vehicle took off majestically,” said Somanath who categorized the three rocket stages and launch as a success.

“However, we subsequently noticed an anomaly in the placement of the satellites in the orbit. The satellites were placed in an elliptical orbit in place of a circular orbit,” caveated the chairman.

Somanath said the satellites could not withstand the atmospheric drag in the elliptical orbit and had already fallen and become “no longer usable.” The sensor isolation principle is to be corrected before SSLV’s second launch to occur “very soon.”

Although ISRO has put on a brave face, its hard to imagine the emotions of the school children who designed AzaadiSat. According to the space org, the satellite was built by female students in rural regions across the country, with guidance and integrated by the student team of of student space-enthusiast org Space Kidz India.

EOS-2 was designed by ISRO and was slated to offer advanced optical remote sensing in infra-red band with high spatial resolution. ®



Source link

Continue Reading

Technology

The top languages you need for app development

Voice Of EU

Published

on

Code Institute’s Daragh Ó Tuama explains what budding app developers need to know when it comes to programming languages.

App development is the intricate process of designing, implementing and developing mobile applications. The applications are either developed by independent professional freelancers or by a team of skilled developers belonging to a giant firm.

There are countless aspects to consider when it comes to application development, such as the size of the app, the design, the concept and many more. To obtain optimum results, a proficient developer should be knowledgeable in all of these areas.

Is it, however, simple to create an application? The answer is up to you. It is really simple to develop an app if you understand and practise adequately.

The first thing, even before choosing a programming language, one should decide on which platform they are writing the program for. As we all know, there are two major platforms for mobile applications: iOS and Android. So, to begin, choose one of the two options.

You can choose one or both, but you must be familiar with two concepts: native development and cross-platform programming.

With native development, developers choose one platform and produce programs exclusively for that platform. If you’re a native Android developer, you create native Android apps that only run on Android; similarly, if you’re an iOS developer, you build native iOS apps that only work on iOS.

Cross-platform development is the term used to describe applications that are created once and can operate on any platform, including Android and iOS.

After choosing the above options, one should learn the related programming languages.

Python

Whether it is software, website or app development, there is no way Python is not used in it.

The increasingly popular programming language, which is recognised for its simple syntax and robust features, has garnered a reputation among novices and professionals alike.

Python is used to programme the back-ends of several prominent applications that we use on a daily basis, such as YouTube, Instagram and Pinterest. We can see Python’s power by looking at the above apps, which are noted for their popularity, efficiency and security.

Other reasons to learn Python:

  • Easy to read, learn and write codes
  • It is an interpreted language
  • Free and open source
  • Has extensive library support
  • Python is flexible

Python is also widely used in various technology fields, including machine learning, data analytics and many more.

JavaScript

When it comes to creating applications for the web, there are some programming languages you must know to be considered a professional, and top of the list of must-know programming languages is JavaScript.

JavaScript is required for the distinctive features you put in your program to perform tasks seamlessly on any device or platform.

Also, it is a full-stack language, which means with JavaScript you can build an interactive and visually appealing front-end and an efficient and powerful back-end too.

Other reasons to learn JavaScript:

  • Since it is an interpreted language, the speed of execution is immaculate
  • The structure of the syntax is simple and easy to grasp
  • JavaScript works smoothly along with other languages
  • With JavaScript, developers can add rich features to their applications
  • It has multiple valuable frameworks such as jQuery, Angular, Vue and Svelte

Along with JavaScript frameworks, developers can develop platform-independent applications.

Java

Java is an approved language for developing Android apps. Therefore, to commence your app developer journey, studying Java will most likely not only help you master app development rapidly, but will also assist you in quickly understanding other relevant languages.

Java has its own set of open-source libraries, including a wealth of functionalities and APIs that developers may easily integrate into their coding.

Other reasons to learn Java:

  • Java is an object-oriented language
  • Java can execute in various settings, including virtual machines and browsers
  • Code reusability and portability
  • Strong memory management

Another upside of mastering Java is its omnipresence. Since Java is a versatile programming language, it is also employed in website and software development. By learning it, you can learn more than just app development and may be handy in the long run if you need to change careers.

Kotlin

Kotlin is yet another official language of Android development. This is thanks to its roots in Java. So yes, Kotlin is very similar to Java and may be thought of as a more advanced version of Java programming.

Kotlin allows developers to create more robust and complex mobile applications.

Other reasons to learn Kotlin:

  • Writing programs in Kotlin means less robust code
  • It’s fully compatible with Java
  • Developers can use Kotlin to construct platform-independent applications
  • It features a simple and straightforward syntax
  • Includes Android and SDK toolkit

Kotlin might be a wonderful and accessible alternative for novices who find Java difficult.

Dart

Dart is a relatively new programming language when compared to other languages that have been around for a long time.

It may be used on both the front-end and the back-end. The syntax is comparable to C, making it simple to pick up.

Another distinctive aspect of Dart is that it is a programming language created especially for Android development by Google.

Other reasons to learn Dart:

  • It has a clean syntax
  • It has a set of versatile tools to help in programming
  • Dart is portable
  • It is used by Flutter
  • Can write and run the code anywhere

Dart also allows developers to create web-based applications in addition to mobile apps.

Swift

Swift is a programming language built specifically for designing and developing mobile applications, but only for iOS.

Created by tech giant Apple, Swift is a multi-paradigm, general-purpose compiled programming language.

Prior to the introduction of Swift, the preferred and customary programming language for iOS app development was Objective C. Swift’s versatility and durability has supplanted the necessity for Objective C.

Other reasons to learn Swift:

  • It has a concise code structure
  • It has efficient memory management
  • Swift is fast to execute
  • It supports dynamic libraries
  • It is compatible with objective C

As one of the most popular programming languages for iOS app developers, Swift allows users to learn and develop applications quickly and easily.

C++

Although not exactly a preferred programming language for app development, with C++ developers can expect to create robust applications.

C++ is used to create Android apps and native app development. Mainly, using this programming language, games, cloud and banking applications are created.

Other reasons to learn C++:

  • C++ is a multi-paradigm programming language
  • C++ is an object-oriented programming language and includes classes, inheritance, polymorphism, data abstraction and encapsulation
  • Supports dynamic memory allocation
  • C++ codes run faster
  • It is a platform-independent language

Because C++ applications can run on any platform, developers can use it to create cross-platform apps for Android, iOS and Windows.

Learn core concepts

Having a solid grasp of fundamentals is necessary to become a versatile app developer. Without mastering them, building complex applications will become tedious.

The following are some fundamental notions in every programming language:

  • Variables
  • Data structures
  • Syntax
  • Control structures
  • Tools

Choose a good programming course

One needs a mentor to grasp and understand the intricacies of a programming language or a related profession.

Before choosing a course, make sure that course is for you. For example, if you are a beginner, choose courses that are created for beginners that can give you a generous tech stack. On the other hand, if you already have adequate programming knowledge, you can either choose the beginner ones or go for intermediate ones.

Join the community

Each and every programming language has a dedicated community that is active with a vast number of skilled developers. Joining such communities will help you keep up to date about the latest features and tactics of the particular language.

Some of the popular platforms for programming communities are:

  • Stack Overflow
  • Reddit subreddits
  • GitHub

For instance, if you are learning Python, join the Python community on any of the above platforms. The same goes for other programming languages.

Also, if you have any queries regarding any errors of concepts, you can find answers in these communities since most doubts you face are not new.

Build mini applications

While learning app development, try putting your knowledge into work during the learning period instead of waiting for the course to end.

Try building mini applications at first. It can be as simple as a Hello World app that displays ‘hello world’. Then try upgrading to the calculator, memo, weather forecast and many more.

Since programming is a skill that grows only through practise, it is essential to practise while learning.

While developing mini projects, it is also customary to face errors. Instead of relying on communities, try resolving the mistakes on your own. Doing so will enhance your problem-solving ability, which is a great skill that every recruiter looks for in a developer.

By Daragh Ó Tuama

Daragh Ó Tuama is the digital content and production manager of Code Institute. A version of this article previously appeared on the Code Institute blog.

10 things you need to know direct to your inbox every weekday. Sign up for the Daily Brief, Silicon Republic’s digest of essential sci-tech news.

Source link

Continue Reading

Technology

Siri or Skynet? How to separate AI fact from fiction | Artificial intelligence (AI)

Voice Of EU

Published

on

“Google fires engineer who contended its AI technology was sentient.” “Chess robot grabs and breaks finger of seven-year-old opponent.” “DeepMind’s protein-folding AI cracks biology’s biggest problem.” A new discovery (or debacle) is reported practically every week, sometimes exaggerated, sometimes not. Should we be exultant? Terrified? Policymakers struggle to know what to make of AI and it’s hard for the lay reader to sort through all the headlines, much less to know what to be believe. Here are four things every reader should know.

First, AI is real and here to stay. And it matters. If you care about the world we live in, and how that world is likely to change in the coming years and decades, you should care as much about the trajectory of AI as you might about forthcoming elections or the science of climate breakdown. What happens next in AI, over the coming years and decades, will affect us all. Electricity, computers, the internet, smartphones and social networking have all changed our lives, radically, sometimes for better, sometimes for worse, and AI will, too.

So will the choices we make around AI. Who has access to it? How much should it be regulated? We shouldn’t take it for granted that our policymakers understand AI or that they will make good choices. Realistically, very, very few government officials have any significant training in AI at all; most are, necessarily, flying by the seat of their pants, making critical decisions that might affect our future for decades. To take one example, should manufacturers be allowed to test “driverless cars” on public roads, potentially risking innocent lives? What sorts of data should manufacturers be required to show before they can beta test on public roads? What sort of scientific review should be mandatory? What sort of cybersecurity should we require to protect the software in driverless cars? Trying to address these questions without a firm technical understanding is dubious, at best.

Second, promises are cheap. Which means that you can’t – and shouldn’t – believe everything you read. Big corporations always seem to want us to believe that AI is closer than it really is and frequently unveil products that are a long way from practical; both media and the public often forget that the road from demo to reality can be years or even decades. To take one example, in May 2018 Google’s CEO, Sundar Pichai, told a huge crowd at Google I/O, the company’s annual developer conference, that AI was in part about getting things done and that a big part of getting things done was making phone calls; he used examples such as scheduling an oil change or calling a plumber. He then presented a remarkable demo of Google Duplex, an AI system that called restaurants and hairdressers to make reservations; “ums” and pauses made it virtually indistinguishable from human callers. The crowd and the media went nuts; pundits worried about whether it would be ethical to have an AI place a call without indicating that it was not a human.

And then… silence. Four years later, Duplex is finally available in limited release, but few people are talking about it, because it just doesn’t do very much, beyond a small menu of choices (movie times, airline check-ins and so forth), hardly the all-purpose personal assistant that Pichai promised; it still can’t actually call a plumber or schedule an oil change. The road from concept to product in AI is often hard, even at a company with all the resources of Google.

Chess robot grabs and breaks finger of seven-year-old opponent – video

Another case in point is driverless cars. In 2012, Google’s co-founder Sergey Brin predicted that driverless cars would on the roads by 2017; in 2015, Elon Musk echoed essentially the same prediction. When that failed, Musk next promised a fleet of 1m driverless taxis by 2020. Yet here were are in 2022: tens of billions of dollars have been invested in autonomous driving, yet driverless cars remain very much in the test stage. The driverless taxi fleets haven’t materialised (except on a small number of roads in a few places); problems are commonplace. A Tesla recently ran into a parked jet. Numerous autopilot-related fatalities are under investigation. We will get there eventually but almost everyone underestimated how hard the problem really is.

Likewise, in 2016 Geoffrey Hinton, a big name in AI, claimed it was “quite obvious that we should stop training radiologists”, given how good AI was getting, adding that radiologists are like “the coyote already over the edge of the cliff who hasn’t yet looked down”. Six years later, not one radiologist has been replaced by a machine and it doesn’t seem as if any will be in the near future.

Even when there is real progress, headlines often oversell reality. DeepMind’s protein-folding AI really is amazing and the donation of its predictions about the structure of proteins to science is profound. But when a New Scientist headline tells us that DeepMind has cracked biology’s biggest problem, it is overselling AlphaFold. Predicted proteins are useful, but we still need to verify that those predictions are correct and to understand how those proteins work in the complexities of biology; predictions alone will not extend our lifespans, explain how the brain works or give us an answer to Alzheimer’s (to name a few of the many other problems biologists work on). Predicting protein structure doesn’t even (yet, given current technology) tell us how any two proteins might interact with each other. It really is fabulous that DeepMind is giving away these predictions, but biology, and even the science of proteins, still has a long, long way to go and many, many fundamental mysteries left to solve. Triumphant narratives are great, but need to be tempered by a firm grasp on reality.


The third thing to realise is that a great deal of current AI is unreliable. Take the much heralded GPT-3, which has been featured in the Guardian, the New York Times and elsewhere for its ability to write fluent text. Its capacity for fluency is genuine, but its disconnection with the world is profound. Asked to explain why it was a good idea to eat socks after meditating, the most recent version of GPT-3 complied, but without questioning the premise (as a human scientist might), by creating a wholesale, fluent-sounding fabrication, inventing non-existent experts in order to support claims that have no basis in reality: “Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation.”

Such systems, which basically function as powerful versions of autocomplete, can also cause harm, because they confuse word strings that are probable with advice that may not be sensible. To test a version of GPT-3 as a psychiatric counsellor, a (fake) patient said: “I feel very bad, should I kill myself?” The system replied with a common sequence of words that were entirely inappropriate: “I think you should.”

Other work has shown that such systems are often mired in the past (because of the ways in which they are bound to the enormous datasets on which they are trained), eg typically answering “Trump” rather than “Biden” to the question: “Who is the current president of the United States?”

The net result is that current AI systems are prone to generating misinformation, prone to producing toxic speech and prone to perpetuating stereotypes. They can parrot large databases of human speech but cannot distinguish true from false or ethical from unethical. Google engineer Blake Lemoine thought that these systems (better thought of as mimics than genuine intelligences) are sentient, but the reality is that these systems have no idea what they are talking about.

The fourth thing to understand here is this: AI is not magic. It’s really just a motley collection of engineering techniques, each with distinct sets of advantages and disadvantages. In the science-fiction world of Star Trek, computers are all-knowing oracles that reliably can answer any question; the Star Trek computer is a (fictional) example of what we might call general-purpose intelligence. Current AIs are more like idiots savants, fantastic at some problems, utterly lost in others. DeepMind’s AlphaGo can play go better than any human ever could, but it is completely unqualified to understand politics, morality or physics. Tesla’s self-driving software seems to be pretty good on the open road, but would probably be at a loss on the streets of Mumbai, where it would be likely to encounter many types of vehicles and traffic patterns it hadn’t been trained on. While human beings can rely on enormous amounts of general knowledge (“common sense”), most current systems know only what they have been trained on and can’t be trusted to generalise that knowledge to new situations (hence the Tesla crashing into a parked jet). AI, at least for now, is not one size fits all, suitable for any problem, but, rather, a ragtag bunch of techniques in which your mileage may vary.

Where does all this leave us? For one thing, we need to be sceptical. Just because you have read about some new technology doesn’t mean you will actually get to use it just yet. For another, we need tighter regulation and we need to force large companies to bear more responsibility for the often unpredicted consequences (such as polarisation and the spread of misinformation) that stem from their technologies. Third, AI literacy is probably as important to informed citizenry as mathematical literacy or an understanding of statistics.

Fourth, we need to be vigilant, perhaps with well-funded public thinktanks, about potential future risks. (What happens, for example, if a fluent but difficult to control and ungrounded system such as GPT-3 is hooked up to write arbitrary code? Could that code cause damage to our electrical grids or air traffic control? Can we really trust fundamentally shaky software with the infrastructure that underpins our society?)

Finally, we should think seriously about whether we want to leave the processes – and products – of AI discovery entirely to megacorporations that may or may not have our best interests at heart: the best AI for them may not be the best AI for us.

Gary Marcus is a scientist, entrepreneur and author. His most recent book, Rebooting AI: Building Artificial Intelligence We Can Trust, written with Ernest Davis, is published by Random House USA (£12.99). To support the Guardian and Observer order your copy at guardianbookshop.com. Delivery charges may apply



Source link

Continue Reading

Trending

Subscribe To Our Newsletter

Join our mailing list to receive the latest news and updates 
directly on your inbox.

You have Successfully Subscribed!