Voting Machines Are Still Absurdly Vulnerable to Attacks

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While Russian interference operations in the 2016 US presidential elections focused on misinformation and targeted hacking, officials have scrambled ever since to shore up the nation’s vulnerable election infrastructure. New research, though, shows they haven’t done nearly enough, particularly when it comes to voting machines.

The report details vulnerabilities in seven models of voting machines and vote counters, found during the DefCon security conference’s Voting Village event. All of the models are in active use around the US, and the vulnerabilities—from weak password protections to elaborate avenues for remote access—number in the dozens. The findings also connect to larger efforts to safeguard US elections, including initiatives to expand oversight of voting machine vendors and efforts to fund state and local election security upgrades.

“We didn’t discover a lot of new vulnerabilities,” says Matt Blaze, a computer science professor at the University of Pennsylvania and one of the organizers of the Voting Village, who has been analyzing voting machine security for more than 10 years. “What we discovered was vulnerabilities that we know about are easy to find, easy to reengineer, and have not been fixed over the course of more than a decade of knowing about them. And to me that is both the unsurprising and terribly disturbing lesson that came out of the Voting Village.”

Many of the weaknesses Voting Village participants found were frustratingly basic, underscoring the need for a reckoning with manufacturers. One device, the “ExpressPoll-5000,” has root password of “password.” The administrator password is “pasta.”

Like many of the vulnerabilities detailed in the report, that knowledge could only be used in an attack if perpetrators had physical access to the machines. And even the remotely exploitable bugs would be difficult—though certainly not impossible—to leverage in practice. Additionally, election security researchers emphasize that the efforts of countries like Russia are more likely to focus on disinformation and weaponized leaks than on actively changing votes. Those turn out to be more efficient ways to rattle a democracy.

But nation states actors aren’t the only people who might be tempted to hack the vote. And a detailed accounting of just how bad voting machine security also underpins a number of broader election security discussions. Namely, state and local election officials need funding to replace outdated equipment and employ specialized IT staff that can update and maintain devices. Voting machines also need stronger security to protect against criminal activities. And election officials need failsafes for voting machines in general, so that a glitch or technical failure doesn’t derail an election in itself.

“For those of us who have followed the state of our nation’s election infrastructure, none of this is new information,” Representatives Robert Brady of Pennsylvania, and Bennie Thompson of Mississippi, co-chairs of the Congressional Task Force on Election Security, said in a statement on Thursday. “We have known for years that our nation’s voting systems are vulnerable.”

Analyzing voting machines for flaws raises another important controversy about the role of vendors in improving device security. Many of the machines participants analyzed during the Voting Village run software written in the early 2000s, or even the 1990s. Some vulnerabilities detailed in the report were disclosed years ago and still haven’t been resolved. In particular, one ballot counter made by Election Systems & Software, the Model 650, has a flaw in its update architecture first documented in 2007 that persists. Voting Village participants also found a network vulnerability in the same device—which 26 states and the District of Columbia all currently use. ES&S stopped manufacturing the Model 650 in 2008, and notes that “the base-level security protections on the M650 are not as advanced as the security protections that exist on the voting machines ES&S manufactures today.” The company still sells the decade-old device, though.

“At its core, a voting machine is a computer which can be compromised by skilled hackers who have full access and unlimited time,” the company said in a statement. “While there’s no evidence that any vote in a US election has ever been compromised by a cybersecurity breach, ES&S agrees the cybersecurity of the nation’s voting systems can and should be improved.”

Congress has worked recently to investigate voting machine vendor accountability, but progress has been slow. In July, for example, only one of the three top vendors sent a representative to a Senate Rules Committee election security hearing, prompting an outcry from lawmakers.

“This report underscores that when you’re using technology there can be a variety of problems, and with something as important as election results you want to get it right,” says David Becker, executive director of the Center for Election Innovation and Research. “The question I hear from the states and counties, though, is just ‘how are we going to pay for it?’ They would love to have skilled IT staff, they would love to hold trainings for their workers, they would love to replace their old equipment. But you can’t just wave a magic wand and do that, you need significant funding.”

Elections officials have made significant progress on improving election infrastructure defenses and establishing channels for information-sharing, but as the midterm elections loom, replacing vulnerable voting machines—and finding the funding to do it—remain troublingly unfinished business.

Source: Wired

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Driverless car makers could face jail if AI causes harm

AI technologies which harm workers could lead to their creators being prosecuted, according to the British government.

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Makers of driverless vehicles and other artificial intelligence systems could face jail and multi-million pound fines if their creations harm workers, according to the Department of Work and Pensions.

Responding to a written parliamentary question, government spokesperson Baroness Buscombe confirmed that existing health and safety law “applies to artificial intelligence and machine learning software”.This clarifies one aspect of the law around AI, a subject of considerable debate in academic, legal and governmental circles.

Under the Health and Safety Act of 1974, directors found guilty of “consent or connivance” or neglect can face up to two years in prison.

This provision of the Health and Safety Act is “hard to prosecute,” said Michael Appleby, a health and safety lawyer at Fisher Scoggins Waters, “because directors have to have their hands on the system.”

However, when AI systems are built by startups, it might be easier to establish a clear link between the director and the software product.

Companies can also be prosecuted under the Act, with fines relative to the firm’s turnover. If the company has a revenue greater than £50 million, the fines can be unlimited.

The Health and Safety Act has never been applied to a case of artificial intelligence and machine learning software, so these provisions will need to be tested in court.

Source: Sky.com

3 ways to make better decisions by thinking like a computer

If you ever struggle to make decisions, here’s a talk for you. Cognitive scientist Tom Griffiths shows how we can apply the logic of computers to untangle tricky human problems, sharing three practical strategies for making better decisions — on everything from finding a home to choosing which restaurant to go to tonight.

How Virtual Reality Will Drive The Future Of Business

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In 1961, the first minicomputer, called the PDP-1, arrived at the MIT Electrical Engineering Department. It was a revolutionary machine but, as with all things that are truly new and different, no one really knew what to do with it. Lacking any better ideas, a few of the proto-hackers in residence decided to build a game. That’s how Spacewar! was born.

Today, the creation of the Spacewar is considered a seminal event in computer history. Because it was a game, it encouraged experimentation. Hackers tried to figure out how to, say, simulate gravity or add accurate constellations of stars and by doing so would push the capabilities of the machine and themselves.

Tech investor Chris Dixon has said that the next big thing always starts out being dismissed as a toy. Yet it’s because so many technologies start out as toys that we are able to experiment with and improve them. As virtual reality becomes increasingly viable, this human-machine co-evolution will only accelerate because, to create a new future, we first have to imagine it.

From Spacewar! To Real War

Growing up in Australia, Pete Morrison always thought he’d be a plumber like his father. His mother, however, had other plans. She noticed his interest in computers and how, from a young age, he spent hours tinkering on the family’s primitive Commodore 64. She pushed him to go to college. Lacking funds to do so, Pete entered the Army to finance his education.

As a Signal Corps Officer, he put his technical skills to good use, but much like the MIT geeks four decades earlier, he soon found himself preoccupied with video games. The military had commissioned a study of simulations at the Australian Defence Force Academy, where he was a student and Pete got involved with testing games. One was Operation Flashpoint, developed by some young geeks at a Prague based company called Bohemia Interactive.

“It quickly became clear that the game could be effective for training military personnel,” Morrison told me. “Before Operation Flashpoint, to train a soldier you had to go out into the field, which was expensive and time consuming. We realized that with this type of computer game, you could design training that would allow them to hone cognitive skills, which would make the in-the-field training that much more effective.”

“Also,” he continued, “because the game was so engaging we got a much deeper level of immersion, which made the training more effective and led the Australian Military to ramp up investments in video games as training tools.”

The Simulation Economy

In the industrial age, experimentation was expensive and unwieldy. Thomas Edison famously observed that if he tried 10,000 things that didn’t work, he didn’t see them as failures, but stepping stones to his next great invention. It was, of course, an ultimately effective process, but incredibly gruelling and time consuming.

Today, however, we increasingly live in a simulation economy where we can test things out in a virtual world of bits and avoid much of the mess of failing in the real world. Consider how today we battle-test different business models and scenarios in Excel. That was much more cumbersome and time consuming when spreadsheets were on paper, so we rarely did it. Now, it’s a routine activity that we do all the time.

As computers have become exponentially more powerful and software algorithms has become much more sophisticated, the usage of simulations have expanded. We use CAD software to design products and structures as well as high performance supercomputers to model weather and even invent advanced materials. When you can try out thousands of possibilities easily and cheaply, you are more likely to identify an optimal solution.

The next era of simulation will be powered by virtual reality and it is almost upon us. Just as Pete Morrison found that ordinary video games could improve tactics in the real world, virtual reality offers the possibility to take training to an entirely new level.

Enter Virtual Reality

In 2005, Morrison left the military and started working directly with Bohemia Interactive. Together, they launched a new company in 2007, Bohemia Interactive Simulations, to focus on the military business. In recent years, the firm has been increasingly focused on applying its expertise to virtual reality platforms like Oculus Rift and Magic Leap.

“The advantage of virtual reality is that we can potentially replace dome projection systems, which cost hundreds of thousands dollars, with a VR system that costs hundreds of dollars and achieve the same or greater level of immersion,” Morrison says. “That can be a huge cost saver for militaries worldwide and revolutionize how we train soldiers”

Yet, like most technologies, virtual reality is quickly moving from high-end early adopters to more mainstream markets. Strivr, for example, got its start by designing virtual reality systems to train $20 million NFL quarterbacks. It now helps train employees at companies like Walmart, United Rentals and Jet Blue by simulating real-life work environments.

Training your employees in a classroom can help teach them basic principles and, in some cases, help build important skills. With virtual reality, however, you can put them in a realistic environment of, say, a sales floor on Black Friday, a construction site or a $50 million airplane at a fraction of the cost. In some cases, training efficiency rates have increased by as much as 40%.

How Humans And Technology Co-Evolve

In recent years, we have come to think of technology in opposition to humanity. We hear that robots are going to take our jobs, that tablets and smartphones are eroding our children’s skills and so on. Yet we often fail to take note of the potential for machines to make us better, to enhance our skills and to make us smarter.

For example, as the digital age comes to an end, we need to invent new computing architectures, like quantum computing, to drive advancement forward. The problem is that, although the technology is progressing rapidly, very few people know how to program a quantum computer, which works fundamentally differently than classical machines.

It was with that in mind that IBM created Hello Quantum, a video game that helps teach the principles of quantum algorithms. “We thought, what better way for those unfamiliar with the principles of quantum mechanics to dip their toe into the topic than through a game? The puzzles are fun, so even those who don’t necessarily plan to study quantum physics will come away with a better understanding of it.” Talia Gershon at IBM Research says.

All too often, we see playing games as just “goofing off,” in order to escape from the “real world.” The truth is that, by allowing us to go beyond our immediate context, games allow us to learn skills that would be difficult, and in some cases impossible, for us to experience directly. That has the potential to enhance not only our skills, but our lives.

The truth is that humans don’t compete with machines, we co-evolve with them. Yes, they make some skills obsolete, but they open the door for us to learn new ones and that can enhance and enrich our lives. As the skills we need to learn increasingly exceed our everyday experience, we’ll find ourselves playing more games.

Source: Digital Tonto

How AI is Revolutionizing Marketing

businessman hand working with new modern computer and business sWith computing power now virtually limitless, the possible applications of artificial intelligence on all aspects of consumer culture likewise seem to be without end. This phenomenon has the potential to launch marketing into a new golden age, and companies that do not take advantage of the new technologies at their disposal are severely limiting their reach and potential. AI and machine learning can be a boon to marketers like no other, but not until these marketers understand how it can work for them and for customers.

AI’s burgeoning presence in marketing not only provides a more satisfying customer experience, but it also boosts marketing campaign effectiveness and opens opportunities for businesses of all sizes and in all industries. Here are four of the myriad ways AI is transforming the marketing landscape for the better.

Personalized Content

Perhaps AI’s most valuable impact in marketing is its ability to understand user preferences and interpret data from them to present consumers with the content most relevant to their interests. Just like Netflix observes what users watch and recommends similar titles accordingly, websites use AI to evaluate extensive amounts of data from users’ browsing habits and present them with information that suits their preferences exactly.

According to Infosys, 86% of consumers noted that personalization impacted their purchases. And 56% of consumers actually expect their interactions with brands to be personalized. Any company that misses out on this opportunity to connect with consumers is running the risk of losing business and customer loyalty.

Predictive Analytics

Predictive analytics can provide marketers with insights into consumer behaviors by creating detailed mathematical models based on data received from each individual customer. A report by Aberdeen Group summarized:

“Predictive technologies can enable more precise segmentation of potential buyers and facilitate a deeper understanding of those buyers, their needs, and their motivations. By optimizing the marketing offer and message directed at these buyers, predictive analytics provides an effective path to delivering better marketing ROI – as evidenced by the superior click-through rates and incremental sales lift.”

Their study revealed a 76% increase in click-through rates through the use of predictive analytics. By programming AI to efficiently carry out these processes, marketing teams can observe the patterns and develop effective strategies accordingly.

Customer Engagement

AI can also be employed to identify certain customer segments that are not as engaged as others, and curate content based on the information it gathers from them. Companies like Dynamic Yield have developed AI engines to acquire insights on each customer through millions of data points that, when analyzed, can determine which customers are loyal to your brand and which ones need more incentivizing. This allows for deeper consumer relationships, where each individual customer feels seen and has their preferences acknowledged. Targeted communications like these have the potential to increase revenue growth by 10-30%, according to McKinsey & Company.

Efficiency and Revenue Growth

It is no secret that AI can outperform humans at many menial tasks. But developing AI systems to take over simple processes can free up the time and capabilities of a business’s human employees, so they can focus more on their specialized skills. Their time can then be redirected to focus on other more complex areas, like content creation, and leave the busy work to the machines. How much does it actually help, you may ask? According to Harvard Business Review and Infosys, the addition of AI has been shown to reduce customer acquisition costs by 50% and increase revenue by 43%.

The possibilities of this new frontier in marketing are vast, as are the rewards. From opening new job opportunities in AI programming, to helping to prevent fraud, the inclusion of computer learning in marketing efforts means benefits for consumers and creators alike.

The numbers don’t lie: There is profit and efficiency in computer learning. Companies that employ it are developing lasting customer relationships and rapidly outpacing their competitors. If you haven’t incorporated AI into your company’s marketing strategy, you’re falling behind. The landscape is being transformed before our eyes—it’s time to take advantage of the new opportunities while they last.

Source: LinkedIn

AI is coming for industrial design

An algorithm could never replace human designers… oh, who are we kidding? We’re screwed.

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MIT researchers have debuted a tool that automatically generates products–and analyzes them in detail–on your behalf.

Take these two task lamps. They each have three heads, bent and placed in very different ways. So which has the better stability? It’s a trick question. They’re equally stable–and that was discovered by an algorithm, which designed them both.

MIT researchers, in conjunction with Columbia University, have unveiled a new tool for designers who work with computer-aided drafting software. Building on previous work over the past year, their technique can optimize a design for any object, like a lamp or boat or wrench, for all sorts of metrics like mass, drag, and stress tolerance. And then it can create dozens of designs of that object, each tuned to different optimal efficiencies.

In other words, it removes iteration from the design process–and it could be applied to the design and engineering of consumer goods and industrial parts, replacing some of the human guesswork of product design and augmenting the intuition of designers themselves.

“A fundamental limitation of typical design optimization techniques is that they require a single objective function for evaluating performance. In most applications, however, multiple criteria are used to evaluate the quality of a design,” the paper explains. “Structures must be stable and lightweight. Vehicles must be aerodynamic, durable, and inexpensive to produce. In most cases, the performance objectives are not only multiple but also conflicting: improving a design on one axis often decreases its quality on another axis. In reality, designers and engineers navigate a complex landscape of compromises, generating objects that perhaps do not optimize any single quality measure but rather are considered optimal under a given performance trade-off.”

Take the humble wrench. Why is it shaped the way it is, with that bulbous head, and a skinny neck? This is exactly what MIT’s system can answer as it generates alternatives to the wrench we all know by reshaping the design to optimize for a myriad of factors at once. Through its generated CAD models, we see that a wrench with the most torque for force would actually have a very elongated and skinny design. But there’s a catch: It couldn’t take much stress. A short, fat wrench could take a lot of stress, but it couldn’t generate much torque. And in fact, right in the balance of all the most important factors–weight, torque, and stress–we see the wrench design we all know.

In other words, this machine logic can do in seconds what people perfected over centuries, arriving at the exact same conclusion–which is validating and humbling at the same time.

But of course, we know how to make a wrench, and we’ve known for a long time. What’s so promising about MIT’s research is that it can work on virtually any CAD model you throw at it, and for the most part, do so within existing workflows. That means it could help designers optimize their existing processes–and, crucially, deconstruct what works and what doesn’t, sooner.

Right now, all these design alternatives are spit out through complex graphs which are hardly navigable to the average person, and the software also isn’t available in any sort of downloadable tool for you to run. (The video you see above is from a different project from last year.) The researchers recognize this–that while they’ve created an AI that might replace a team of designers, getting it to work well for those designers is another challenge altogether: “One important consideration comes from the human-computer interaction…what is the best way to display it to an engineer who must digest the space of candidate designs?” they ask in the paper’s conclusion.

Could we become overly reliant on a single system with a single approach to design optimization, and perhaps lose some of our own creativity and ingenuity in the process? Indeed, how exactly AIs work with designers of tomorrow is the billion dollar question behind the future of design–let alone the world’s next great wrench.

Source: FastCompany

Here’s What You Really Need To Know About Blockchain

In 1970, a scientist at IBM Research named Edgar F. Codd make a remarkable discovery that would truly change the world. Though few realized it at the time, including IBM, which neglected to commercialize it. It was called the relational model for the database and it would spawn an entire industry.

Yet while today few have heard of relational databases, everybody seems to be talking about blockchain. Much like Codd’s idea nearly a half century ago, blockchain represents the opportunity to create a new data infrastructure, which in turn, is likely to help power business for another half century.

Still — and very contrary to the current hype — few of us will ever work with a blockchain or even know it is there. The real revolution will come not from the technology itself, but from its secondary effects in the form of new business models. To leverage these though, you will first need to understand how Edgar Codd created the data economy in the first place.

How Relational Databases Changed the World

Imagine taking a trip back to 1980. Ronald Reagan was just elected President, and Terry Bradshaw led the Pittsburgh Steelers to yet another Super Bowl. Just a year before, Larry Ellison and two friends launched the first commercial product based on Codd’s ideas. Two years later, they would change the company’s name to Oracle.

Now imagine trying to explain to someone in 1980 what they would use a relational database for. Back then, few people used computers, which were primarily used for back office tasks and heavy computational jobs like scientific research. Very little that relational databases did was relevant to how people worked back then.

What made relational databases important is how they changed how we could manage data. They made data fungible. Classical or “flat file” databases worked very much like an Excel spreadsheet. They stored data in a columns and rows which lacked flexibility. You really needed to know how the database was set up to find the information you needed. Anybody who has tried to understand someone else’s spreadsheet knows what that’s like.

With relational databases, however, all you need to know is the query language and you can extract what you need from any database, no matter who set it up. That’s why today, we can hop on a system like the Internet and pull data from just about anywhere we want. It’s what made the information age possible.

Why Blockchain Matters

Relational databases were designed for centralized computing. Data was stored in a mainframe and we would use a terminal — and later a PC — to get information out. For example, executives use ERP software to pull data from far-flung operations and manage business processes more effectively. Marketers access research databases to understand consumers. Salespeople leverage CRM systems to service their customers.

Today, however, computing is no longer centralized, but radically decentralized. We carry smartphones in our pockets that are more powerful than what would have been considered a supercomputer back when relational databases were invented. We use those devices not only to retrieve information, but also to send it to centralized databases, often without knowing we’re doing it.

That creates an information bottleneck that is often insecure for a number of reasons. First, while most commercial databases are encrypted, data needs to be unencrypted for us to use it, which leads to problems like the one with Facebook and Cambridge Analytica. Data is also unencrypted at the source, so firms can access our data and store it without us having any control over it.

The most salient aspect of blockchain is that it functions as a distributed database. Unlike relational databases that house data in one location, blockchain distributes data everywhere at once in a secure form. So we can track data wherever it goes, what it’s used for and see who alters it in any way. That will create a radically more transparent information economy.

What a Killer Blockchain App Will Look Like

In a recent conversation I had with Bernie Meyerson, IBM’s Chief Innovation Officer, I asked him what he was most excited about. Thinking he would talk about the Watson program or a futuristic research project, I was somewhat surprised that the first thing he mentioned was his company’s joint venture with Maersk to develop a blockchain infrastructure for global trade.

With everything going on at IBM, from artificial intelligence to developing new computing architectures like quantum computing and neuromorphic chips, shipping seemed a bit low brow to me. Nevertheless, once I started digging into the numbers I could begin to see what he meant. Blockchain really can have an extraordinary impact on global trade.

Consider the fact that a 2013 study by the World Economic Forum found that reducing back-office friction in international trade could increase GDP by nearly 5% and commerce by 15%. Global GDP amounts to about $80 trillion, which means you’re talking about a $4 trillion potential impact. If even a fraction of that pans out it’s huge!

The thing is, nobody is going to buy a product and say, “Wow! This is 5% cheaper thanks to blockchain!” The truth is that no one will ever see it. Blockchain, much like the relational databases that came before it, is technology infrastructure. It’s basically like paved roads were to cars — an absolutely essential enabling technology, but not a “killer app.”

Where to Find the Next Big Thing

Over the next decade, we’ll see the impact of blockchain unfold, but it will look a lot more like the IBM-Maersk joint venture and Oracle than the next Google or Facebook. If you don’t work with a relational database now, you probably won’t have much to do with blockchain in the future.

Still, that doesn’t make the impact any less real or exciting. Much like the Internet distributed computing, blockchain will distribute secure data and that is likely to radically increase transparency and security while reducing costs. “Disintermediation” is a term we can expect to hear a lot of in the future.

For example, Hu-manity.co is a new startup that plans to give patients more control over their health data. Today, when we sign a consent form for our data to be used for research, we essential give it away. However, with blockchain, we will be able to track it, decide for ourselves how we want our data to be used and even be reimbursed for it.

So if you want to know how to profit from blockchain, start looking for information bottlenecks, like global shipping or medical data. Eliminating those bottlenecks is how blockchain will truly change the world.

Source: digitaltonto.com