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AI looks at itself in a mirror and sees a biological human

Reaching the technological singularity: What will happen when machines become smarter than us?

  • min read
    • Picture a world where machines have long since passed the Turing Test. When people can't tell the difference between artificial and human intelligence. In this world, will humans become obsolete, like the floppy disk? Or is it a world where artificial and human intelligence merge to usher in the next step of our evolution? That's the technological singularity, the theoretical time in the future where technological development outpaces our abilities to maintain control over it. For better or worse, it's undoubtedly something that will result in never seen changes to our world. When will we reach technological singularity? Join any discussion about business technology, and it won't be long before someone mentions artificial intelligence (AI). The amount of digital data in the world has already surpassed our abilities to manage it, a development, which has made clear the need for algorithms to do the job for us. But algorithms and AI aren't the same things. They're not even close. An algorithm is a computer program that parses data from sets too large for human interpretation, whereas true AI is capable of thinking for itself, making its own decisions, and having free will. True artificial intelligence doesn't exist. Yet. The explosion of AI has long been a popular trope of science fiction, but the reality is edging ever closer. Ray Kurzweil, world-renowned futurist and Google's Director of Engineering, believes technological singularity is going to happen before 2045. Take note: so far, an impressive 86 percent of his predictions made since the '90s have become a reality. But even if that sounds optimistic (or pessimistic, depending on how you look at it), it's the logical next step in the evolution of technology. Once computers can learn for themselves without being taught and trained with data that we've collected, we can expect profound society changes on a scale never before seen. How can we keep up with technology? The emergence of an artificial superintelligence would bring us greater inventive and problem-solving skills than humans are capable of. This might lead to the creation of a new 'species,' one which might not necessarily have human interests at its heart. But so far, we're nowhere near developing machines with human-level intelligence and the ability to make decisions independently. Now we can enhance human intelligence and capabilities with tools and technology. Indeed, that's precisely what we've been doing for hundreds of thousands of years, ever since our ancestors figured out how to light fires. But today, the stakes are much higher with projects like brain-to-computer interfaces as active areas of research. This may, in turn, lead to radical developments like mind uploading, in which you entire consciousness can be uploaded to and stored on a machine. At least, it could be useful for those millennia-long interstellar voyages. Such developments might sound dystopian since they have the potential to transform what it means to be human ultimately. At the same time, the convergence of technology and humanity, an area of study known as transhumanism, appears to be the only way we can keep up with the constant pace of change. It's either that or the machines eventually outpace our natural evolution and take over. The survival instinct If we can't keep up, and technological evolution ends up outpacing our capability of keeping control over it, then we could face profound risks to security. Each of the seven characteristics that define every biological organism comes together to form a singular goal – survival. Technological singularity occurs when AI matches our desire and capability to survive as a species, and there's little reason to think that AI would have any evolutionary motivation to be friendly to humans. Today, machines are programmed by us to create the outcomes we want. But what happens when they're capable of programming themselves? Time for Humanity 2.0? Imagine you're an expert on machine learning, working on an artificial intelligence algorithm that will be able to create other AIs by itself. Are you merely training your replacement, or have you become an architect of your obsolescence? Welcome to Humanity 2.0, where the human condition is no longer about our biological form. It's a time when machines become an integral part not just of our societies, but also ourselves. Conversely, this could also lead to newer and deeper social class divisions than we've ever seen, as people are separated into those who are augmented by technology and those who are not. There's a safety benefit of not being physically augmented by technology – no one will be able to hack into your mind. How humans could remain our biggest threat Perhaps it isn't machines that we should be worrying about at all. Assuming we'll always be able to control artificial intelligence, we could end up transforming it into a deadly weapon. It's already happening. The proliferation of social media, for example, is one of the most significant security and privacy concerns this century. That's not so much down to vulnerabilities in the technology, but because it's a highly effective medium for conducting social engineering attacks. Coupled with the rise of AI, cybercriminals will be better equipped to analyze social media conversations to emulate users' writing styles and craft more convincing messages to their victims. Even before we reach the era of true AI, the ability of algorithms to parse enormous amounts of data is already misused. On the one hand, algorithms can perform many useful tasks to improve our world, such as climate modeling and disaster prediction, while things like neural interfaces can help victims of spinal cord injuries. On the other, the ability of humans to interfere with information systems makes them a more significant threat than the technology itself. Perhaps it would be better if we just let the machines take over in the hope they'd nurture us like the irresponsible children we are. To fear or not to fear Ray Kurzweil has a more positive outlook than most about the singularity. He claims it's an opportunity for humankind to improve by making us smarter and better at all the things we value. Other science and technology leaders, including Elon Musk, Bill Gates and the late Stephen Hawking, are not so sure. But one thing's for sure – there will come a time when our augmented descendants look back on us as rather quaint and exotic creatures. While we're still decades away from the singularity, it presents itself as the next big step in the evolution of technology. Emerging tech, like autonomous vehicles and facial recognition, are already paving the way. True artificial intelligence is coming sooner or later, which is why it's time for the business world to start taking AI ethics and security issues seriously. It's no longer the concern of generations far in the future. This article reflects the opinion of its author.
non surgical technologies

Surgery has risks. Medication has side effects. Is technology healthcare’s ‘third way?’

  • min read
    • In 2017, a research team from a Finnish university did a controlled trial on a common shoulder surgery technique. One group of patients had 'sham' surgery, where the surgeon simply made an incision then stitched it up again, while another group received the actual surgery. The team found the real surgery performed no better than sham surgery. This was no obscure operation being performed by rogue surgeons: "Nearly 21,000 decompression surgeries [were] done in the UK every year, and ten times that many in the United States," said adjunct professor Simo Taimela, University of Helsinki research director. Their research findings highlight an alarming fact: As many as half of common operations are not supported by reliable evidence that they work. While some surgeons still perform this operation (the placebo effect gives patients a better chance of recovery than no treatment,) is it rational to expose these patients to the risks of surgery? One in six patients develop post-surgical complications, and one in 35 patients die after surgery worldwide. The people that died might've been already seriously ill, but then again, the most common post-surgery complication is something preventable: Infection. Surgery is not the only way to treat illness that carries inherent risk. For the many benefits, medications come with downsides. If it's effective, it probably has side effects too. Side effects and their likelihood depends on the medication, but in general, they range from tolerable but annoying (rashes, dry mouth, itching) to life-changing (seizures, migraines) to life-threatening (allergic reaction, cytokine storm.) Technology is increasingly providing ways to prevent and treat illness that mean patients don't need to take the risk of surgery or face medication side effects. These recent examples show technology helping us live longer, more pain-free lives. Brain surgery without scalpels Stereotactic radiosurgery uses light beams to target cancer cells in brain tumors without making any incisions. The light beams break down malignant cell DNA and create an inhospitable environment for the tumor. For some cancers, it's as effective as surgery, but without the pain and recovery time. TED Medical guide to stereotactic radiosurgery As well as brain tumors, the technology has been successfully used on some lung, pancreatic and liver cancers. It treats animal cancers too. Unashamedly evoking visions of the future with its spaceship-like design, gyroscopic radiosurgery further refines the technique, using gyroscopic motion to more accurately direct light beams at the tumor. Flagging errors in prescribing medicines Doctors are busy. Errors in prescribing medication – such as wrong dosage or not noticing a patient's medication allergy – are behind a surprising number of hospital admissions. Around 1 in 10 hospital prescriptions and 1 in 20 general practice prescriptions contain potentially harmful errors. After deep-diving into the reasons behind prescribing failures and what other countries have tried to improve practice, the UK started implementing software that's making a big difference. Pharmacist-led Information Technology Intervention (PINCER) scans patient records and flags risky prescribing. Since its introduction in 2018, risky prescribing has fallen by nearly 15 percent. The biggest successes of PINCER might be ahead, with roll-outs where most prescribing errors happen: On hospital wards and in emergency departments. Diagnosis and treatment by artificial intelligence In 2019, The Lancet published a systematic review and meta-analysis (grouping data from many studies to make for more dependable analysis) showing artificial intelligence (AI) has become reliable enough to be considered an accurate source of diagnosis. This may mean more accurate and earlier diagnoses, avoiding the need for surgery or medication. Pharmaceutical company Bayer is also working on software that uses AI to help diagnose complex and rare conditions and develop medications to treat them. The software could help identify more quickly whether medication works. "We're looking at how we can identify the right patients and sites to run our clinical trials. We would be able to run shorter studies and show where the medication is the right one for those patients earlier," said Bayer's head of AI projects, Angeli Moeller. Some AI-enabled diagnostic innovations haven't gone so well. Since 2017, the UK's National Health Service (NHS) has trialed an AI-enabled chatbot and remote consultation app, GP at Hand. Its implementation was plagued with claims that the app's AI was making mistakes. One doctor wrote to the British Medical Journal (BMJ) to point out that safety and effectiveness claims were based on shaky evidence. To answer concerns, one council commissioned an independent study. The study found the app was viewed positively by users and doctors but was unlikely to benefit the most vulnerable patients because users tended to be young, affluent and in good health. High numbers of users were choosing to stop using the service, but one potentially positive outcome: When connecting with patients through the app, GPs were less likely to prescribe medication. With known over-prescribing problems around antibiotics and antidepressants, less prescribing could be a good thing for our health. 'Delocalizing' care with technology COVID-19 has meant many are now familiar with over-the-phone or video consultations with doctors and specialists. A raft of innovations alongside this shift are helping make remote patient care more effective. One example we recently covered is the StethoMe digital stethoscope. It lets doctors receive digital waveforms of a patient's breathing and heartbeat over the internet. Then there's Ochsner's iHealth Ease Cuff, a blood pressure-measuring arm cuff that connects to a patients' smartphone over Bluetooth, sending results straight to their doctor. The theory was that frequent monitoring and feedback would help patients manage their condition better and let professionals intervene in problems earlier. Trial results seem to back this up, with double the number of patients in a group that received the cuff achieving their health goals compared with the group receiving conventional care. Recently approved by the US's Food and Drug Administration (FDA,) Clarius technology enables faster ultrasound scanning in a wider range of settings. The wireless ultrasound scanner connects to an app on tablet or smartphone, making high-quality ultrasound more portable. Paramedics and non-specialists can transmit live scans to a specialist who can review them in real time. The app also uses AI to adjust scanning parameters, reducing user error and the time taken to get results. All this means faster, more accurate diagnosis, and no need to take the patient to the equipment. There is constant development of new technologies that can improve patient health without surgery or medication. Greater investment in healthcare research and innovation thanks to COVID-19 may further speed the arrival of these new ways of doing medicine at a hospital or clinic near you. In the laudable rush to do everything we can to save and improve lives, we must not lose sight of the data security and privacy implications of these new technologies. Technologies often do most good in the hands of people who've traditionally had less access to healthcare, like older patients and those in isolated areas. This, in itself, makes the need for cybersecurity awareness among patients and practitioners all the more acute. There is no need to hold back innovation, but there is a need to involve the right people in testing and design, and when it comes to cybersecurity, educate, educate, educate.
AI future jobs climate

Far from AI stealing our jobs, the future could be full employment

  • min read
    • In the run-up to the coronavirus pandemic, future-gazing economists were increasingly concerned with technological unemployment: The idea we'll replace human workers with automated systems, creating social unrest among surplus workers. If the prospect of guillotine-wielding retirees didn't worry you, the economists asked who would buy what robots built. Workers would stage uprisings, unable to buy anything on their way home from the barricades. It wouldn't be the first time automation has brought social unrest – the term 'Luddite' comes from a movement in the UK's industrial revolution that saw workers destroying machinery that was replacing them. Calming the angry masses Many have proposed solutions to this (speculative) crisis, most prominently Universal Basic Income (UBI:) A regular pay packet for every person, with no means testing. It would lubricate the economy's supply side and dry up the reservoirs of guillotine-building fuel. UBI may be a good idea, but not because of technological unemployment. The poster child for technological unemployment is trucking. Artificial intelligence (AI) boosters will tell you being a trucker is one of the most common jobs in the US and that self-driving trucks are within our grasp. But there's more to the story. "Trucker" is one of the most common jobs in the US because the Bureau of Labor Statistics deems "trucker" includes anyone who operates a delivery vehicle, from a van to a long-haul 16-wheeler. Self-driving vehicles are nowhere near ready to replace urban delivery drivers. It's true that if given their own lane on major highways, automated lorries could probably navigate reliably. How exciting. We've just invented a substandard freight train. Bigger fish to fry That all said, I'm a science fiction writer. My job is to imagine six impossible things before breakfast. I can conduct a thought experiment in which AI breakthroughs replace most of the jobs we're doing today. If that day comes, it will be a good one. It will free up people for the most urgent task our species faces – a project that will absorb all the work all of us and all our descendants can do, for centuries to come. I'm talking about climate change. Remediating climate change will be unimaginably labor-intensive: Relocating every coastal city inland, building high-speed rail to replace aviation and treating runaway pandemics, to begin with. If automation takes your job, you'll have another. You'll have ten. Or a hundred. Fantasy faces facts Perhaps you think I'm dodging the question. If we're stipulating a fundamental breakthrough that produces AI, what about a comparable geoengineering breakthrough? Maybe our AI will be so smart it will work out how to reduce Earth's reflectivity, creating mass cooling to counter the greenhouse effect. That's less science fiction, more science fantasy. It's too late to halt the climate processes that will flood every coastal city, displace hundreds of millions of people and sicken billions as pathogen-bearing organisms seek new habitats. These things will happen regardless of geoengineering. Why? Consider, for example, the heat we've sunk into the oceans. The seas won't cool until the energy trapped in their depths is expended. The ice caps are toast. I can speculate about AI all night long, but thought experiments that repeal the second law of thermodynamics aren't scenario-building. They're wishful thinking. Pandemic lays economic assumptions bare The COVID-19 crisis has taught us two critical things. The first: Ideological commitment to government austerity doesn't build capacity – it destroys it. California spent 200 million US dollars in 2006 to stockpile N95 masks, ventilators and mobile hospitals. In 2011, the state dismantled its stockpile to save just 5 million US dollars a year on maintenance as its tax revenues fell after the subprime crisis. The second: Sovereign currency issuers don't have cash shortfalls during crises – they have capacity shortfalls. Since the pandemic began, central bankers of countries with monetary sovereignty (issuing free-floating currencies and not borrowing substantially in currencies they don't issue) have been creating money. They just type zeroes into a spreadsheet at the central bank, and the money appears. Orthodox economics says creating money produces inflation, but it didn't, because the money was largely spent on things the private sector wasn't buying. Inflation happens when the amount of money in circulation grows in relation to goods and services for sale. When the amount of currency goes up without a rise in available goods and services, you have more money chasing fewer things, and prices rise. But the private sector had suddenly developed a disinterest in the labor of hundreds of millions of workers worldwide. When governments gave those workers money to cover their overheads, they're not competing with the private sector, so they're not driving up the price of labor. There's one exception. The demand for healthcare has spiked, with no end in sight. Central banks can print as much money as they want, but they can't make ventilators appear on the market. California gave away its ventilators 12 years ago to save money. The US central bank could have filled California's budget hole 12 years ago. Instead, they "saved" money and threw out the health infrastructure. Likewise, post-2008 austerity saw a drawdown of medical infrastructure across the European Union, particularly in the poorest countries like Italy, whose early collision with the coronavirus was made brutal by austerity-wrought brittleness. Austerity is a bad bet. Money is the one thing central banks can't run out of. But we sure can run out of ventilators and masks. A better kind of full employment If governments with monetary sovereignty can make as much cash as they need to buy things nobody else is buying without creating inflation, we won't have an automation-driven unemployment crisis. If robots can supply all the capacity for our needs, central banks can distribute money and tax some of it away if the ratio of money to stuff-you-can-buy gets out of line. Today, we have a kind of full employment. You can either be employed, underemployed or unemployed. The prevalence of unemployed and underemployed people controls inflation by keeping wages down. If you ask for a raise, your employer can threaten to fire you and hire someone unemployed. A better kind of full employment would take every person made idle by the climate emergencies we're about to face and give them essential, meaningful work for a decent wage. They'd be moving cities, building seawalls and fighting pandemics. They'd remediate habitats and care for refugees. No one is competing to hire others to do that work, but it's work that will need doing, and there'll be work enough for all. Dreaming gets real Those fretting over technological unemployment are worried about a future, speculative moment when we go through a technological singularity, driven by general AI through nebulous means. That's a long way away, but in the next year or so, the pandemic crisis will end. About 30 percent of all jobs that existed before the crisis will no longer exist. The workers who did those jobs will be unemployed unless governments step up. Governments will collapse if they choose unemployment over job creation, leaving a third of workers jobless and desperate. They may take their nations down with them. Nations with 30 percent unemployment can't function and can't create capacity that will make them resilient to the next crisis. It's a civilization-threatening downward spiral that will end the dream of creating superintelligent AI. Societies whose primary industry is digging through rubble for canned goods do not make AI breakthroughs. The 30 percent unemployment "solution" solves nothing, but what about a job guarantee? The 30 percent working under government jobs programs will have working lives decoupled from the market. The movement of markets – especially financial markets – will be irrelevant to everyone except chart-watching, twitchy nerds. Can we imagine a job guarantee? You might find it hard to believe we'll find the political will to procure the labor of people without jobs. Even in the face of a climate emergency that needs labor to prevent destruction of cities and civilization, it's hard to imagine such an ambitious program. But if we're going to dream, let's dream big. I see a path from here to a job guarantee. I don't see a path from here to AI. Universal Basic Income or job guarantee? Universal Basic Income (UBI) is easy to understand. If some people don't have enough money to make ends meet, give everyone money. That's a great idea, but it won't address inequality. If most people lack the money to keep body and soul together, giving them a monthly stipend could make the difference between starvation and subsistence. However, for the small number who already have money enough to save, that stipend is just additional savings. Ten years later, the masses have averted starvation, while the wealthy minority have accumulated much extra wealth. A job guarantee is also easy to understand. If we have a job that needs doing and a person that needs a job, we should pay the person to do that job at a sufficient wage with decent benefits. This will address inequality and create a true minimum wage. Without a job guarantee, the real minimum wage is $0 per hour: The amount you get if you want to work, but no one will give you a job. Early 20th-century economist John Maynard Keynes once proposed we could jump-start an economy by paying half the unemployed people to dig holes and the other half to fill them. No one's tried that, but we just spent 150 years subsidizing digging hydrocarbons out of the ground. Now we'll spend 200 to 300 years subsidizing our descendants to put them back in. Since neo-liberal economics swept the globe in the 1980s, business has used unemployed people to control wages. This tactic is no longer sustainable. The pessimistic future of your business is unemployment so high it will collapse the property relationships and rule of law your market relies upon. The optimistic future is simply that your firm hires in a way that makes it competitive with good public sector jobs remediating our planet.
how to spot hype

Many hats make light work: How to see the truth hidden by tech hype

  • min read
    • Hype is increasingly playing a big part in the worst decisions around technology. Investors and journalists alike fell for Elizabeth Holmes' shiny pitch for her non-existent blood testing technology Theranos. Fitness tracker Jawbone, once valued at 3 billion US dollars, was among several wearable tech firms to go spectacularly belly-up as it became clear the market wasn't as expected. Seeing past puffed-up narratives and getting to the core of claims means making more informed decisions around technology, investing in the most promising next steps and building a business strategy with concrete foundations. You needn't necessarily wade through the mountains of information we're faced with every day to spot hype. As a science and tech journalist and advisor for Innovate UK and the European Commission on start-up funding, my job is to work out what's hype and what's not. Here are six ways to hone your critical thinking and better understand the complexity, adapted from my recent book, Smoke and mirrors: How hype obscures the future and how to see past it. 1. Understand what hype is for Most people think hype is a bad thing, and it can be a problem in that it sends us along the wrong path. But there's more to it. If 'consensual' fooling is what magicians do (you go to a magic show to be fooled,) 'non-consensual' fooling is lying. Hype could be considered 'accidental fooling' – the messages are not intended to misinform, but the person receiving them can easily get the wrong end of the stick. Hype is powerful. People use it because they're keen to get out what they think are important messages. They want to grab your attention. For example, government health guidelines intend to inform society. They might use hype to ensure people receive the message loud and clear. When you remember what hype is for, you may see a message differently. Ask yourself why the communicator might use hype. Are they trying to sell something, convince you to change your behavior or to expose a problem? Think about why and how the message got in front of you when interpreting it. 2. Find the nuance Many headlines and start-up pitches include absolute statements, like "AI will revolutionize health" or "robots will replace farmers." Statements like these are shortcuts to establish a jumping-off point. They're not necessarily false and normally contain a trace of truth, but they miss out a lot in their simplicity. To find the nuance, say to yourself, "Well, it depends." You're not denying another's expertise but getting yourself into a questioning state. When you start from a place of "Well, it depends," your mind can better explore scenarios that test the statement. For example, "It depends what kind of job I have as to whether a robot will replace me." Just three words can open your mind to nuance in absolute statements. 3. Look for your fanaticism If you work in tech, you're probably a techno-optimist: Someone who believes technology is a force for good. We wouldn't have much innovation without this excitement and optimism, but we need to be conscious of where our excitement turns into fanaticism. Emotional desire can blind us to the reality of societal impact. We can build and promote things that may be harmful to those unlike us. It's more than putting ourselves into other people's shoes; it's taking a step back and asking, "Do we need this?" We know technologies can evolve and lead to related industries, becoming a problem over time. We should ask ourselves these questions in the development process to prevent unintended consequences down the line. Another question to prompt this thinking is, "If this technology were to grow in mainstream adoption, what would be the dystopian science fiction novel written about it?" 4. Wear more hats Searching for the right or wrong answer is hard when faced with a complex question like, "Is AI good or bad?" There's a lot to consider. You need to 'map' the full system of ideas somehow, know the different scenarios in which the question must be answered and draw a conclusion. A simple way to start gathering the nodes is to wear different hats, one by one. Put on your own hat. What does the question mean in your personal life or business? Then put on your supplier or your management team's hats: What does it mean in theirs? Then consider working in a country not your own, in a different industry or being someone working 50 years in the future. This is a way to remove your blinkers and so, see past the hype. 5. Work out what's curbing action Hype can make people think someone else is solving things, so they don't need to do anything. For example, in the food industry, hype around vertical farms and lab-grown meat lets us 'off the hook' of changing their habits. The 'tech will save us' mentality stops us making more considered decisions about what we eat today. We can also work out, beyond the narratives laid in front of us, what fears or cultural tendencies build ideas in peoples' minds they're unwilling to confront. For example, the phrase "We'll find a cure for cancer" exudes hope, tenacity and courage. But it also shields fear that we still have far to go, and, of course, fear of death. This fear and, in some cultures, the taboo against talking about death, means it's become anathema to question those working in this field. It feels like doubting those on the front lines. Our trust rarely wavers. It's not necessarily a bad thing – many efforts in cancer therapeutics are successful – but when anything is culturally sacred and immune to criticism, much-needed constructive ideas are stifled. Consider the culture and public sentiment around the topic you're investigating. 6. Work out who is responsible right now Hype can sometimes give us narratives that relinquish our responsibility around technology. For example, "Robots will steal our jobs." It's an easily spread and understood idea, with an element of truth, but lacks important detail. If you take away the hyped narrative, realize it's a comment on the future of automation and rephrase it to "Company executives are going to choose to replace human jobs with robotic machinery," another element becomes clear. Instead of talking about Terminator storming in to claim your job or the technological singularity and when it might be achieved, immediate discussions around responsibility, decision-making and societal economics come to the fore. We need to work out who is involved in the technology's development right now and ensure their responsibility isn't hidden beneath simplified, hyped narratives. While decision-making in business is often the hardest part of the job, spotting hype in technology – and so preventing yourself and your business from succumbing to it – needn't be difficult. When you next come across a headline you think may be hyped, following these steps will help you make better decisions.
ai government

Could AI improve the way a government runs its nation?

  • min read
    • PwC estimates that artificial intelligence could increase business productivity by up to 14 percent by 2030; this could prove to be very useful than the public sector. Or governments, to be precise. As our world and society begin to respond to increased threats, from climate change to crime sprees, governing organizations are constantly looking for ways in which to make the most of their often-stretched resources. Now, with the introduction of AI, there are more opportunities to streamline their operations with the help of machines. But how will it improve the way the public sector works? Let's take a look. Starting a digital conversation A *relatively* simple AI development that could make federal and local government customer and information centers more efficient. As our societies become more connected, the ability of local governments to reduce face-to-face interactions and phone calls by using digital chatbots could free workers up to do more focused work. Case in point: the US Citizenship and Immigration Services of Department of Home Land Security recently developed EMMA. Named after the poet Emma Lazarus, this chatbot handles requests on immigration services, green cards, passports and other departmental services. Available in both English and Spanish, it manages one million interactions a month. Other examples include TravelBot – an AI-powered Messenger chatbot launched by Transport for London in the UK. It helps users with travel information within the city, including bus arrival times and service updates. Even the Infocomm Development Authority of Singapore is jumping on the trend. They're working with Microsoft to develop conversational interfaces (bots) for their Smart Nation initiative. Connecting the dots Digital connected devices – also known as the internet of things (IoT) – could have a considerable part to play in the future of government. The city of Jacksonville, US, piloted "intelligent streetlights."  These use cameras connected to the lights to track traffic and pedestrian movements and decide when to dim or brighten each lamp. Sensors in these lights can also give people a heads up as to where parking spots are. The cameras also helped police keep the streets clear and raise revenue by catching more parking violators per month. Darting through data Identifying patterns in large datasets is hard. Not least because it takes a long time, but because we're human, and there's a chance we might miss something. Machine learning (ML) is a fundamental aspect of AI: the ability for computers to learn, analyze and act on data at a rapid rate. And its use in the public sector has been proven by the Southern Nevada Health District (SNHD) in a recent pilot project. SNHD carries out inspections relating to food poisoning cases. In recent years, instead of random checks, they deployed a machine learning software to analyze data from Twitter. Specifically posts from people claiming to have food poisoning from a restaurant, and collated them into high-risk venues. During investigations, they found an average of nine failings out of ten against the average of six for random inspections. Research estimates that if every inspection were adaptive, it could result in 9,000 less food poisoning incidents each year in the US alone. Bringing in the admin bots You might immediately think of robots here, but it's far more than that. Robotic process automation (RPA) is more on the money. RPA involves using software, or bots, to automate tasks rapidly. Anything from processing application forms to filling out endless spreadsheets – RPA can do it all. It's best suited for repetitive, predictable and time-consuming tasks – all the work your staff probably hate doing. Automation means dramatically reducing the time spent on admin and freeing up more time for skilled work. How can governments keep cyber-secure? There are many ways in which AI could change the way our governments work, but we need to be careful. As the government and public sector begin to experiment with AI, there are several challenges towards successful, widespread adoption. Budget restraints and employee training and security – notably the need for active cybersecurity measures with AI programs and developments – could be stumbling blocks. Perhaps the most important of those is security, and as governments deal with masses of sensitive data daily, AI could enable new ways for hackers to infiltrate government systems. If AI supports publicly-funded services, like the military or healthcare, hackers who compromise these systems have a higher capacity to do considerable damage more quickly than before. AI uptake is one thing, but how will we protect our governments, and more importantly, our data, in the age of the machine?
industrial ai

Evolution not revolution: What to expect from AI in manufacturing

  • min read
    • From assembly robots to automated warehouses – artificial intelligence (AI) and the manufacturing industry are a match made in heaven. And this just the beginning. According to a report by IDC, by 2021, 20 percent of leading manufacturers will rely on embedded intelligence (using AI, internet of things and blockchain applications) to automate processes and speed up execution times by up to 25 percent. How is AI improving the bottom line? Microsoft's recent research explains how that's benefitting firms: manufacturing businesses in the US using AI are performing 11.5 percent better than those that aren't. AI benefits industry. Why? Its possible applications are extensive. And the stats are enticing. According to McKinsey, 50 percent of companies that invest in AI over the next five to seven years will have the potential to double their cash flow; manufacturing is leading the way due to its heavy reliance on data. In a recent Forbes insight survey on AI, 44 percent of respondents from automotive and manufacturing think AI will be important to manufacturing in the next five years, while almost half (49 percent) said it was critical to success. From real-equipment maintenance to more streamlined design processes, let's take a look at some AI-led trends. The age of human-robot collaboration https://www.youtube.com/watch?v=7GdiN6KmGCc Poor maintenance can reduce a manufacturing plant's overall productive capacity by as much as 20 percent. No wonder then, that industrial robots with embedded AI are proving popular will manufacturers. KUKA, the Chinese-owned German manufacturing company, is one of the largest manufacturers of industrial robots in the world, and they're making slick new movements. KUKA claims their LBR iiwa "is the world's first series-produced sensitive robot," which "signals the beginning of a new era in industrial robotics, where humans and robots can work side by side in close co-operation." No threat of robots outmoding us then, they claim. And it's working. LBR iiwa units are used at the BMW Group Plant in Munich, Germany, where they're helping workers insert components into car bodies during construction. Next-generation software design It's a new way of looking at things: designers or engineers enter their ultimate design goal into generative design software, complete with cost constraints, preferred materials and methods. The software then takes the original idea and explores different solutions to make it a reality on the spot. The result: you get many design possibilities, a conclusion of whether they'll work or not, and a recommendation for the best solution. It could be used from everything from aircraft wing design to plastic molds for a phone case. Brian Matthews, Vice President of Platform Engineering at Autodesk, US, says it could achieve 50,000 days of engineering in one day. Connecting the dots with smart manufacturing Put simply, smart manufacturing is the use of industrial IoT devices – those connected through the internet, able to send and receive data – and AI. According to a report by TrendForce, smart manufacturing could be worth over $320bn in 2020. And some of the big players in the industry are already well ahead in this trend. Siemens launched Mindsphere in 2016, its smart cloud for industry, allowing manufacturers to monitor machinery. For example, its latest gas turbines, packed with over 500 sensors that monitor their condition from temperature to pressure, allowing for real-time updates when they need repairing. General Electric has a similar idea with their Brilliant Factories, which utilize AI tracking and processing in the manufacturing process to find issues as they emerge. These factories are powered by Predix, their industrial IoT platform; it uses sensors to automatically capture every step of the process and monitor each piece of equipment. And the results speak for themselves. 18 percent improved equipment effectiveness at a factory in Pune, India, and a 25 percent increase in on-time delivery rates at a jet engine factory in Muskegon, US. Predictive maintenance becoming part of the program Predictive maintenance. In short: manufacturing machines repairing themselves, without our help. One step towards the era of robots? Perhaps. A load off the factory manager's mind? Definitely. IoT sensors and advanced analytics make it so, and it's set to save businesses heaps of time and resources. It's already in action. A report by Deloitte explains how predictive maintenance is built into machines used for manufacturing titanium implants. Let's break it down: titanium is so strong that it requires tools with diamond tips to cut into it, but often these tips become dull from overuse. In comes predictive maintenance, which uses the sensors and monitors to assess the shape of the machinery. In turn, they can give a warning to engineers when the tips need to be changed. Eagle-eyed supercomputers Similarly, as impeccable as our eyes are, they miss things. We're only human. This is why startups like landing.ai have developed machine-vision tools to find microscopic defects in products like circuit boards. These tools use machine-learning algorithms trained on remarkably small volumes of sample images. They can detect not only minor defects but also send immediate alerts to manufacturers. Evolution, not revolution Automation and machine learning have been around in the manufacturing sector for decades, so these developments are an evolution, not a revolution, for the industry. If you've not thought about how AI could affect your business or even your competitors, now's the time to explore it. Greater industrial connectivity, more sensors, powerful analytics and robots (to name a few) bring distinct challenges. Aime Lachapelle, of Emerton Data, says that 85 percent of the companies they surveyed aim to implement AI into their production processes. However, less than 30 percent have an AI development plan. While many manufacturers have aspirations to implement AI into operations, thorough research and training are needed beforehand. It's time to optimize your cybersecurity These technologies have the power to change the manufacturing industry for the better. But with new technology comes new risks. Anton Shipulin comments:
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