Tag Archives: AI

Technology delivers, if our expectations are realistic

If you believe the tech industry hype, then you may have heard some of the following: The robot cars are coming! The robot cars are 15 years away! You should hold off on the purchase of your next vehicle! Sadly, people are usually pretty awful at predicting tech, especially when it’s the exciting tech that most of us want. It will most likely be at least two vehicle purchases before you can afford a self-driving one.

Despite the lack of immediate gratification, patience is a virtue and technology does deliver in the end. Perhaps the most famous technological disappointment is the flying car. Back to the Future was decades ago, but the flying car—not to mention the hoverboard—has yet to materialize for us.

Beware the tech industry hype cycle

Had you, for example, told everyone in the 1980s that by 2010 we would all be walking around with flat, touchscreen supercomputers in our pockets—with an always-on wireless connection to the sum total of human knowledge—most people would have thought you were a sci-fi author, a dreamer, or just insane. That’s the sort of nonsense everyone was promised by the tech industry hype of the 50s and 60s, and it never materialized.

Even into the 90s, the idea that someone would successfully combine a cell phone, music player, video player, and computer into one gadget seemed like a fantasy. We could barely keep a cell phone that was “just a phone” operating for more than a couple of hours, and they were the size of bricks. Today, smartphones are cheap enough to be ubiquitous even in the poorest countries and are often the only computer people have.

Enterprise technology analysts at Gartner have created a concept called the hype cycle to describe this phenomenon. A technology trigger leads to some research and development. This creates first generation products. Everyone gets excited by the possibility and hypes it up. The first gen products don’t live up to expectations the technology falls into the “trough of disillusionment.” From there subsequent generations of products are developed, refining the technology so it’s better able to meet market needs. Adoption grows and plateaus as pressure builds to make radical alterations to the technology.

This serves as the tech trigger for another hype cycle, and we’re off to the races one more time.

Technology we’re just plain ruining

Ever been oversold something by a salesman? Ever had a friend talk up a blind date so much they had zero percent hope of living up to it? Hype is the worst, and it stands to tarnish some amazing technology before it even really exists. Machine learning, for one, has a lot to offer the world if we just let it incubate a little longer. It has revolutionary potential and just about nothing to show for itself—yet.

To put it into a simple equation, over-hype + premature, inflated investments + unavoidable under-delivery = a bad taste in the mouth of investors who aren’t like to reinvest again. You know, when the tech innovation is actually a thing. And just like that, you’ve stunted technological advancements that could have hugely benefitted the world.

One tech innovation currently at the peak of a hype cycle is artificial intelligence (AI). If you listen to the so-called visionaries, we’ll all have fully sentient Cylon servants inside a decade and be able to upload our consciousness into a virtual reality paradise shortly after that. That’s all so far beyond our current technological capability that it’s downright laughable.

AI won’t be built within our lifetimes. This isn’t to say it’s impossible to construct; rather, we are woefully deficient in both the basic research into the kinds of multi-dimensional neural networks and the materials technology required to build the type of brains required to house that level of consciousness.

Despite the tech industry hype, we won’t be seeing the flying car of the AI world. Oh well. AI in some form or another has become an invisible part of the world around us, and most of us don’t even realize it. It’s in the automatic braking system of our cars. It makes the advanced radios in our cell phones possible. AI runs everything from radar systems to the flight systems that let military drones fly even when operators are halfway around the world.

Modern tech is A-OK

Yes, the robot vacuum you bought at the retail store isn’t the brightest, but 25 years ago it would have been the pipe dream of a madman. That vacuum’s larger cousins help run a robot hotel and serve as robot submarines. It’s okay to be excited about groundbreaking tech concepts, but let’s be reasonable and not set ourselves up to be disappointed long before it ever becomes real life. Instead of frowning forward waiting for new innovations, look back and marvel at how fast we’ve advanced. Imagine life before cell phones, the internet, or even TV . . . Roomba seems pretty dope now, doesn’t it?

The current tech industry hype will give way to natural language and computer vision systems that allow computers to see in a manner similar to us and to understand what we mean when we talk. From there, very real products will develop and they will integrate themselves into every aspect of our lives. So don’t buy into everything that every “visionary” dreams up as absolute truth, but don’t be completely disillusioned either. Technology marches on as widget by widget becomes just another part of our daily lives.

AI health care innovation: Coming to an operating room near you

Traditional computer coding has built virtually every aspect of the modern world—but because the goal is to teach computers how to solve problems, it’s constrained by the limits of a coder’s own skill and imagination. On the other hand, AI coding is about teaching computers problems, and allowing solutions to arise from simple, roughly evolutionary processes. This shift has allowed AI to surge forward, and has become probably the key health care innovation of the past several decades.

The impact of this sort of alien computer insight is just now starting to be felt. From online diagnosis to early robotic surgery, health care AI is already allowing incredible gains in health care efficiency and safety. At the same time, health care is becoming a more demanding market by the day. Continuing to advance health care in this scenario will require even greater gains for AI than we’ve seen yet.

Health care is sick, and AI could be the cure

For a long time now, health care has been getting less affordable, a problem Forbes claims can only be fixed with “systemic” solutions. Partly, the problem arises from rising rates of chronic illnesses like diabetes, partly from falling rates of company-sponsored health care packages, and partly from the ever-increasing lifespans of first-world citizens. This problem seems intractable: As health improves, the cost of improving health increases. But AI is built to smash through those sorts of barriers.

AI solutions are already assisting doctors (or taking over for them entirely) in a wide range of specific diagnoses like pneumonia and heart attacks. Meanwhile, AI-driven robots have already begun to work their way into the operating room, improving on human-level safety while allowing paid surgeons to focus on more difficult tasks. These are the sorts of labour-saving innovations that could revolutionize health care—much in the same way that technology previously revolutionized manufacturing. It could allow the industrialization not of medicine alone, but of care itself.

How to assist (and exploit) the AI revolution

As in all things, it will almost certainly fall to the IT department to make the high-level aspirations of executives actually come true. Sure, management needs to keep eyes on such large-scale strategic ideas, but it’s the ITDMs who often end up having to push for these cutting-edge AI solutions. It ultimately comes down to ignorance of the technology itself, and IT workers will often have to take the unfamiliar step of pitching an AI tech based mostly on its ability to affect the bottom line.

Still, spending decisions get made at the highest levels, and it’s ultimately IT’s job to implement those decisions. That means the same thing in the context of AI as anywhere else: security. Human surgeons are, of course, not hackable—their performance and discretion aren’t affected by the ongoing arms race between hackers and security researchers, while AI surgeons very well could be. Even a small number of damaging stories in the media could tank much of AI’s momentum, branding them not as a cost-reducing new health care innovation, but as a digital spy privy to the public’s most sensitive personal information.

AI-driven change has only just begun

What’s striking about the heath care AI technologies in development right now isn’t so much the depth of any one ability, but the breadth of the field as a whole. In many cases, there’s nothing inherently harder about diagnosing one disease versus another, but each process takes time and effort to teach. The potential of AI in health care will only be truly realized when many different AIs can collectively perform the majority of lower-level tasks that are currently given to new med-school graduates. At that point, it’s just a matter of collecting these relatively simple insights into one cohesive, self-administrating package.

This could end up being the end-stage role of human doctors and surgeons—to intelligently direct and deploy an array of AI-based solutions, and sift their responses. It can only happen, however, if the roll-out continues in a safe, secure manner that doesn’t cause a backlash in public opinion. That responsibility falls to the tech workers of the world, both in and outside of health care itself.

What the future of automation holds for modern-day Luddites

The future of automation is advancing every day, as Uber tinkers with its fleet of driverless cars and robots neatly cart Amazon products through the company’s shipping centres. On one hand, it’s exciting to imagine a world enhanced by digital assistants and ambient computing. But on the other, automation’s potential impact on employment freaks us out—just a little.

The Brookfield Institute for Innovation + Entrepreneurship at Toronto’s Ryerson University anticipates that artificial intelligence could eliminate as much as 42 percent of Canadian service-sector jobs within the next two decades. How will workers respond to changing conditions in this digital version of the Industrial Revolution? Here’s what the Luddite experiences of the 19th century can teach us about what to expect.

The future of automation

We’re witnessing history repeat itself, as it often does. The Luddites were the first group of workers put out by automation, so they struck out against the machines.

Although automation’s impact is and will continue to be global, North American jobs prove uniquely vulnerable, because many of them are located in service-sector industries ripe for automation—like manufacturing, transportation, finance, and food service. Professions like trucking and tax preparation could be eliminated outright. AI may even come to an IT team near you.

Just like in the Industrial Revolution, businesses are beginning to see how they can enable workplace productivity and boost profitability by automating jobs previously held by humans. This will trigger significant job losses, and it might depress wages in certain industries, too. According to Argentus, manufacturing will be hit the hardest: A new study estimates that up to 45 percent of manufacturing and supply chain positions could be automated in the coming years.

How the government, advocates, and industry feel

Although the new administration is in its early days, its public statements regarding automation may not be reassuring to those concerned about the potential impact on employment. The Huffington Post wrote all about how experts have criticized Employment and Social Development Canada for not taking the issue seriously. There are also calls in Canada and abroad to consider instituting a universal basic income, in which all citizens are guaranteed a base-level income, regardless of their employment status.

No matter what you may think of that particular idea, we’re undeniably at an inflection point, where there’s a critical opportunity to prepare the global workforce and economy for the AI-powered future. But if we don’t take meaningful steps soon, the social and economic disruption will be severe.

Modern Luddites’ first step?

At first, the Luddites said they weren’t against tech advancement. They just wanted to share in the profits or secure a common agreement on how workers’ livelihoods would be preserved in the new order. No big deal.

When this failed, they turned to sabotage, attacking and destroying the frames used to weave stockings. But the Luddites ultimately faced legal crackdowns from parliament and even public hangings—which extinguished their rebellion. We argued a lot about this concept of “fair profit” in the 19th century, and the modern-day conversation will only intensify.

Raging against the machine

If employers respond as dismissively to calls for economic fairness as their early 19th-century counterparts did, workers might begin voicing their concerns about the future of automation using more dramatic methods.

It might be harder for them to immediately locate a physical target, though. Weaving frames are long gone; instead, virtual AI workers will silently perform their tasks in the cloud—intangible and out of reach. But that barrier won’t prevent workers from taking out their frustrations on the nearest real-world object representing their economic deprivation, be it a driverless car or a manufacturing floor robot.

We can also expect a massive outpouring of creative public protest via social channels. The Luddites did something similar in their day. Smithsonian Magazine points out that they created the myth of abused apprentice Ned Ludd, writing songs about him and signing humorous letters in his name.

North American workers will leverage their ingenuity and acerbic wit to drive home their point about how the productivity gains of the information age must benefit all, not just a privileged few. If employers aren’t receptive to these appeals, they’ll find themselves shamed in today’s public square: social media.

Adapting to the automated workplace

As this debate rages, some entrepreneurs will adapt on their own. We saw a mid-century example in the movie Hidden Figures, when Dorothy Vaughan saved her team of female computers from NASA layoffs by learning FORTRAN and teaching it to them.

Contemporary citizens can capitalize on the coming tech transformation to establish professional fields that never existed before. A raft of new job categories emerged several decades after the Luddites’ struggle, in fact, bringing prosperity to later generations. But a piecemeal approach, in which workers must fend for themselves, won’t be enough in the short term. We could still be setting the stage for an unruly period during which people who feel they’ve been left behind begin to fight back.

The automated future and its impacts aren’t yet fully understood. But once workers see how they’ll be affected, they’ll demand a meaningful conversation. The topic at hand? How to make sure technology advancements give everyone cause to celebrate—not just those at the top.

Next-gen tech steps up to the plate

Some next-gen technology has been on the verge of going mainstream for years but never quite seemed to make it—until now. 3D printing, virtual reality (VR), and artificial intelligence (AI) have finally arrived.

The Spiceworks 2017 State of IT report, which surveyed almost 900 IT decision-makers, lists these technologies at the bottom in terms of technology trends adoption. That’s unsurprising, given their nascent status and the fact that companies struggle to understand their commercial potential. But the market is starting to show enough interest to make them significant, and the level of tech development in each of these fields is stunning.

According to the report, 7 percent of respondents currently use 3D printing, with another 5 percent plan on using it. Four percent already use VR, with another 3 percent coming on board soon. Just 2 percent of respondents use AI, with another 3 percent planning to use it—although this may be a special case. AI is increasingly embedded behind the scenes as an enabling technology in applications and services that don’t necessarily foreground it as a feature.

How have these technologies developed in the last few years, and which companies lead the pack?

3D printing

3D printing’s been around since the invention of stereolithography in the early ’80s, but it was traditionally restricted to large firms that could afford the expensive industrial equipment. Then, the first open-source, self-replicating printer, the RepRap, was designed. In 2010, MakerBot debuted its 3D printer, followed in 2013 by Formlabs with its Form 1.

As desktop 3D printing evolved, so did the techniques. MakerBot uses deposition printing, which involves layers of material printed atop each other. Conversely, Formlabs brought stereolithography to the lower end of the enterprise market. In 2016, the industry moved on. MakerBot was acquired by high-end material jetting firm Stratasys in 2013, leaving XYZprinting, Ultimaker, and M3D leading the market for personal 3D printers, as noted by CONTEXT.

Several companies are now focusing on innovation in 3D printing. HP, for instance, is hitting the enterprise market with its 3D-printing technology and operates a marketplace in which third parties can innovate with their own printing materials. Imagine printing parts with embedded components, such as layers with different colours, LED indicators, and even circuitry. Executives envisage a future where 3D-printed parts can report their own stress and thermal conditions by directly connecting to the Internet of Things.

Virtual reality

Modern computerized virtual reality experienced a series of false starts. In 1991, arcade-game firm Virtuality launched its rudimentary VR-based arcade system, and four years later, Nintendo tried its hand in 1994 with the Virtual Boy, but the computing wasn’t fast enough, and the displays weren’t good enough. The next-gen technology lay largely dormant until 2012, when Oculus VR launched a Kickstarter crowdfunding program for its Rift head-mounted display.

The Oculus demos suggested the technology had finally evolved to support virtual reality, and while commercial delivery was delayed, Facebook acquired Oculus for $2 billion two years later, demonstrating its own faith in the concept. Since then, things have exploded on the VR scene. Powerful smartphones with high-resolution displays create impressive VR experiences at the low end of the market, thanks to a partnership between Oculus and Samsung, while HTC provides a similar offering with its Vive headset. Google also jumped aboard in late 2016 with its Daydream VR software platform, followed shortly after by Microsoft, which also unveiled a VR device.

VR is complemented by its cousin, augmented reality, in which computer imagery enhances rather than replaces images of the real world. Microsoft’s Hololens captured the public’s imagination in this space and is now on sale to developers. While Google’s Glass AR system was discontinued, it led a $542 million investment in secretive AR firm Magic Leap, which since expanded its total funding to $1.4 billion—not bad for a company that hasn’t even shipped a product yet. There’s a lot of money floating around the VR and AR market, and the next five years promise unprecedented growth for AR and VR as hyper-scale companies integrate them with a plethora of back-end services.

Artificial intelligence

Toward the start of this decade, the three biggest stories in AI hinted at where we’d end up five years later. IBM’s Watson defeated human contestants in the game show Jeopardy; Google revealed its driverless car technology was already on the road for months; and Apple launched Siri, its digital assistant. Since then, these technologies have all evolved.

Companies constantly push the boundaries in their AI research. Last year, Google’s DeepMind AI division won a game of Go against the world champion in a coup that wasn’t expected to happen for years. Self-driving cars are well on their way to commercial reality, with Elon Musk’s Tesla halfway there already—although ironically, Musk has voiced his concerns about AI’s potential to run away with itself and threaten human existence.

Microsoft, Google, and Amazon all jumped on the AI-assistant bandwagon, integrating them into equipment that listens to you as you roam around your home. The idea is to make AI so easy to access that it becomes part of your everyday life, accessible wherever you are. That’s part of AI’s biggest promise and, potentially, its biggest danger: As it becomes increasingly sophisticated, it promises to permeate our lives without us even aware of what’s happening.

It’s been a wild five years for these three technologies, but now that they’ve arrived, the most important part of their journey is only just beginning. What they’ll deliver in 2021 will likely be more amazing still.

Smart grocery stores are here—but are they more smartphone or smartwatch?

First there were smartphones. Now there are smartwatches, smart homes, and even smart grocery stores. Smart technology keeps evolving, but it hasn’t always done so in a straight line. While the evolution of smart technology has meant that some devices (like smartphones) have become ubiquitous, others (like smart fridges) have failed to catch on.

From Amazon to Apple

In December, Amazon opened Amazon Go, a smart grocery store where there are no lines and no cash registers, according to Forbes. Customers scan their phones when they enter the store, take products off the shelves like they normally would, and when they’re done shopping they simply walk out the door. No more scanning required. Amazon says the store uses computer vision and sensors combined with artificial intelligence to figure out what people are taking and bill them accordingly. It’s a sign that the evolution of smart technology is moving beyond individual devices and into physical spaces.

Amazon isn’t alone when it comes to using smart technology in the physical world. Some professional sports teams have started adding location-based features to their apps, allowing them to, say, send a message to every app user who’s at a game. Some brick-and-mortar retailers are experimenting with technology that will allow them to communicate with customers based on what aisle of the store the customers are standing in, giving them access to product reviews and sending them deals based on what’s right in front of them.

Smart technology has already had some pretty big false starts. The Apple Watch remained a niche item, especially considering it’s an Apple product. It suffered from lacklustre sales and a generally indifferent consumer market. The rest of the smartwatch industry has seen pretty similar results—popularity with a small subset of fans but little mass-market interest. Is it because people already gave up their wristwatches for smartphones and don’t want to go back? Or because smartwatches are essentially a peripheral purchase? Given the fact that the Apple Watch didn’t get a Dick Tracy-style video-communicator app until November 2016, it’s possible consumers just didn’t see what problems the technology was supposed to solve.

Heading into the home (and garage)

That’s what happened to the first generation of smart-home technology. It turned out that people weren’t interested in buying an internet-connected fridge just for the sake of a having an internet-connected fridge. But the current wave of smart-home technology is a different story. People understand the appeal of a lighting and heating system that can be automated or controlled remotely. And as the cost of these systems continues to decline, they’ll continue to become more attractive to consumers.

The smart-home market is also getting more interest from tech giants. Apple, Google, and Amazon are all pushing smart-home solutions, says Forbes. For all three companies, AI assistants will be a key part of this strategy. After all, using a voice command to turn up the heat on a winter day is pretty cool. If these companies are successful, it will revolutionize the way users interact with their smart homes. While Google and Apple have phone-based AI assistants that their customers carry around with them, Amazon has positioned the Echo as a stand-alone device, albeit one that can control other devices.

The other big smart technology on the horizon is the smart car. For companies like Uber, this could be a game changer, but in general, autonomous cars may be a difficult market to crack for smart technology. Some people will want to continue driving, while others will be worried about losing their jobs. Most other smart-technology setbacks have been the result of consumer indifference, but the smart car could see real pushback.

It’s hard to say where the evolution of smart technology is heading, but it seems safe to say that a lot more devices—and places—will be getting a little dose of intelligence sometime soon.

4 ways chatbots can cure your app fever

The most useful robot personal assistants in 2017 don’t look like The Jetsons’ robotic maid, Rosie. Instead, they’re invisible outside their artificial intelligence (AI)-driven impact. The popularity of chatbots in external and internal business contexts is exploding—and we’re pretty excited about it. Even if these bots can’t personally bring you a perfect cup of tea, they can revolutionize the way you collaborate.

Chatbots are service-oriented tech “powered by rules and sometimes artificial intelligence, that you interact with via a chat interface,” in the words of AI entrepreneur Matt Schlicht. Amazon is just one brand to jump with both feet into supporting access to bots. In fact, they’ve recently opened their machine-learning AIs behind their image-recognition products, text-to-speech functionality, and Alexa, the virtual assistant for the Echo.

Chatbots are wicked convenient and have the potential to support massive productivity gains within your organization. Your team can turn your distributed productivity and collaboration apps into a single, cohesive network with robots. (Yes, we’re in the future, now. Welcome.)

Incoming: Chatbots

Most of us have already had a few interactions with messaging bots and other forms of interactive language recognition. The technology is incredibly sophisticated, and its amazing capabilities definitely aren’t limited to academic research labs. According to Laurie Beaver, research associate for Business Insider:

  • Chat AI can currently have “increasingly engaging and human” conversations.
  • With messaging interactions, bots are an intuitive component of the mobile experience.
  • You can also utilize chatbots to build a robust ecosystem of interactive apps.

In fact, the technology is now so widely available that 20 million people are interacting with the friendly Xiaoice (pronounced Shao-ice), Schlict reported. When a journalist interviewed this “17-year-old girl,” she displayed normal human reactions, such as embarrassment and ultra-realistic language processing. She’s so popular that tech director Yongdong Wang described her as “the largest Turing test in history.”

Putting AI to work

IT pros aren’t the only ones tired of juggling project management software, team chat, email, reporting tools, and every other app on their plate. It’s a fever affecting every knowledge worker—and it results in damaged efficiency from switching between apps when all you wanted to do was manage information and collaborate with coworkers.

IT pros may be the guiltiest of the “slap an app on it” approach to solving workplace needs, but too many platforms can drag down your productivity. Netskope research found the average enterprise employee used 935 apps—within a single quarter. Ouch. Fortunately, chatbots can help with your not-imaginary feelings of app fatigue.

1. Efficient data retrieval

How many times have you needed to log into your VPN, open the right app, and click around to simply remember when a deadline was coming up? Info retrieval and verification are major parts of the work we do, especially in the off-hours at home and when we’re answering employee questions.

With messaging-based bots, your process to double-check when your endpoint security risk analysis is due to your boss is going to get a lot faster. Chat-based bots are built to quickly scan and retrieve data across multiple apps, so reconciling information between several platforms could soon become obsolete as they improve at complex queries. How much time could you save if a messaging robot answered all your questions for you?

2. Contextual info

Messaging isn’t the only function of chat AI that can drive collaborative value. Imagine if you were messaging a coworker about an ongoing project and the chatbot pulled contextual info directly into your app. Your bot could instantly remind you that you aren’t actually available for an 11 a.m. meeting on Tuesday, because your calendar says so. It could also silently insert a link to a document you’re discussing with a project partner. With context-based language processing, they’re happy to do the legwork for you.

3. Help desk ticketing and user insights

What if your complaints to a coworker were silently transferred into an error report by a chatbot and escalated? In fact, what if the bot’s natural propensity for language processing allowed your IT to actually understand how your users feel about your applications and tech? Understanding trends in end user complaints and compliments would paint a richer picture of how people actually feel about your business solutions.

As Forbes‘ Daniel Newman points out, AI can make the distinction between simple issues and problems that need escalation to a real, live human. Your help desk employees may never take another call about how to restart a computer again!

4. Joining disparate apps

Manual user setup is a chore, and information inconsistencies are the worst. App fatigue can mean sloppy data consistency, and expensive integration isn’t always the right answer. Besides, poor identity governance isn’t just confusing and a barrier to collaboration—it’s a real risk for your organization. Chat-based bots can push user information and permissions out to all the right apps. They can also let you know if your project management and budgeting software contain conflicting information.

Not only does the idea of chat-based AI acting as a cloud over your organization boost productivity, it may also have security benefits. A bot’s ability to recognize unusual behaviours from a device or user profile could help you connect the dots if you’re ever faced with device theft or brute-forced user credentials by a cybercriminal.

Countless organizations and knowledge workers have hit the app ceiling—the point at which their productivity and collaboration tools have become more of a barrier than a benefit. If going through the machine learning motions to build a chatbot from the ground up sounds like a massive undertaking, you’re right. Fortunately, there are a few businesses working to deploy affordable, ready-made solutions for you.

Montreal excels at tech innovation—and creativity

Montreal’s five-year mission to become a smart city has proven to be a success. Canada’s second largest city has earned the title of 2016 Intelligent Community of the Year, due in large part to creativity and innovation, according to the selection jury. And it’s not over.

The largest French-speaking city in North America embarked on its smart city plan in 2011 as part of a focused effort to transition its economy to information communications technology (ICT), aerospace, life sciences, health technologies, and clean tech. Though greater Montreal’s economy was heavily impacted by the decline of heavy industry in the 1980s, these new sectors now include 250 companies employing about 10 percent of its workforce.

Montreal was given the distinction of being named 2016 Intelligent Community of the Year after an evaluation based on six Intelligent Community Indicators, as a well as a seventh, “From Revolution to Renaissance,” which was the annual theme of the Intelligent Community Forum (ICF). The ICF jury concluded that the city was top of the innovation game and was a striking example how the organization sees cities entering a new renaissance by leveraging revolutionary technology.

What it takes to make a smart city

According to ICF co-founder Lou Zacharilla, Montreal succeeded by being true to its DNA, which gave birth to a powerful games industry and Cirque de Soleil. Being named 2016 Intelligent Community of the Year is a reflection the city’s continued investment in urban initiatives like the Quartier des Spectacles, where broadband and ICT have a key role in enabling the economic and human expression of culture.

This mix of business and the arts made Montreal distinct as an Intelligent Community, the jury noted, and can be attributed in part to its unusually rich mix of private-sector, public-sector, and social enterprise innovation. Like many cities, it has accelerator programs and co-working spaces to foster an expanding start-up culture, but Montreal has made sure arts and media play a significant role in that ecosystem.

The city is home to a large-scale Innovation District populated with accelerators, incubators, and university campuses. Included in this district is the independent, non-profit tech accelerator InnoCité MTL, which receives both city and business financial support and has already fostered more than 15 startups in just over a year. Together, these entities have nurtured the growth of new companies and attracted the innovation units of larger established companies.

Creativity is shown as a priority with Montreal’s efforts to combine its smart city efforts with its Entertainment District. This district mixes interactive public art and public performance space with a cluster of theatres, music halls, and nightclubs. It also takes advantage of social enterprises, like arts installations and urban farming.

One action plan, dozens of innovations

As with all smart city plans, Montreal’s was incremental and included expansion of its wired and wireless broadband infrastructure. This was thanks in part to the city-owned electric utility, which has helped achieve an 81 percent internet penetration rate that is mostly high-speed. These innovation efforts also included the deployment of technology to support collaboration among residents, businesses, and institutions, as well as to make Montreal’s services and systems more efficient.

But what really got the ball rolling? The journey started with The Montreal Smart and Digital City Action Plan introduced in 2015, which builds on consultations with citizens and city workers, and a review of international models to identify needs, issues, and priorities of the city. That process took place in the fall of 2014, and led to the identification of five focus areas:

  1. Economic development: Promote growth of leading-edge sector by employing the smart city strategy as a catalyst for bringing the project to fruition and as an engine of economic development
  2. Urban mobility: Optimize mobility throughout the island in real time
  3. Direct services to citizens: Increase the provision of direct digital services to citizens and businesses
  4. Way of life: Develop spaces supporting urban innovation and diminish the digital divide
  5. Democratic life: Improve access to democratic life and bolster the culture of transparency and accountability

Montreal’s action plan also identified four structural elements necessary for the transition:

  1. Telecommunications: Develop ultra high-speed, multi-service telecom infrastructure
  2. Open data: Release and use prioritized open data
  3. Architecture: Create an open, interoperable, technological architecture
  4. Community: Develop solutions to urban issues in co-creation with the community

Put your office tech to the test

Take a good, hard look at your office infrastructure to see where innovation is needed the most. Consider every idea, no matter how grand or simple. There was no shortage of ideas generated by the consultation process of The Montreal Smart and Digital City Action Plan. A whopping 232 ideas were selected to become 70 projects within six programs based on several criteria: their impact on structural components; contribution to stated strategic orientations; scope, including impact on citizens; their cost, effort, and return on investment; and their implementation period. Of the projects selected, 26 were expected to take less than a year, 38 would require one–three years, and six were more long-term initiatives that were projected to take more than three years.

No infrastructure innovation happens overnight, be it in a big city or your own data centre. Montreal’s projects included several smart mobility and parking projects, as well as a high-speed, fibre-optic Scientific Information Network. The city also operates six Learning Labs with areas of focus ranging from transportation to healthcare and urban planning. These Learning Labs launched an online collaboration system to engage the 5,000 companies in its ICT cluster to encourage open innovation.

Montreal’s strategy has put a particular emphasis on artificial intelligence (AI). Along with the academic community and the Province of Quebec, many AI startups are being mentored and a number of projects are underway—including ones to improve traffic flow and transportation safety in the city. Ask yourself if your infrastructure would be smarter with some much-needed new tech, the way Canada’s smartest city is with AI.

Montreal’s transformation is an example of how creativity and collaboration are critical to innovation, especially on grand, city-sized scales. How can your office tech get a little smarter?

4 “productivity” tools that might actually create more work

If you are anything like the average Canadian smartphone owner, your app usage has probably declined over the last few years. People are simply downloading fewer apps and deleting them more frequently. This means the mobile, physical, and virtual tools you interface with need to be great, useful, or both—but are top productivity tools secretly time-wasters in disguise? Dr. Alexandra Samuel is a tech journalist and self-proclaimed “super adopter” who samples hundreds of apps, networks, and devices on a yearly basis. Despite her “thousand-app lifestyle,” she’s a firm believer that less is more. “For most people,” Samuel says, “the goal is to adopt the smallest number of tools necessary to work efficiently.”

Despite IT pros’ love of gadgets and apps, there’s a point where technology can distract instead of enhance (crazy, we know). Do you really need an app to blow out birthday candles for you? Or an expensive notebook to take notes in meetings? You may want it, but you most likely don’t need it In the spirit of getting more done, we’ve curated a list of four productivity tools that might actually create more work.

1. Phree

Phree recently raised $1,000,000 via a wildly successful crowdfunding campaign for the world’s first “unrestricted, high-resolution, write-virtually-anywhere mobile input device.” In plain English, Phree is a Bluetooth-enabled pen that allows you to scribble notes on your desk, your wall, or even your arm. OK—it’s a pen without ink.

This innovation certainly has some cool applications, especially for artists. Notes you take from anywhere can be instantly transferred to any Bluetooth-connected device. However, could it really help you get more done? We’re excited to see how this product is received in the real world, but we can’t say it’ll be more efficient than voice-to-text note recording directly into your mobile device. Either way, it’s cool, and that’s the whole point.

2. Moleskine Pro

About a year ago, the cult-favourite notebook brand Moleskine announced a new notebook designed specifically for productivity. The Mokeskine Pro offers both notebook and paper-based planner features, a blank table of contents, adhesive sticky tabs, and much more. There’s even blank sections formatted for common work-based applications, including meeting minutes and brainstorms.

Unfortunately, reverting to paper-based note taking can represent a step backward for most of us. Even if you’re dedicated enough to painstakingly fill out the table of contents in your new notebook, is it more efficient than dumping meeting agendas in your company’s cloud? Paper notes have a magical way of going missing, especially when they’re most needed. And the last thing any of us need is something else to keep track of.

3. Moorebot

An “adorable, yet creepy” robot that wants to be your best friend? With a single, giant blinking eye that rotates to maintain unbroken eye contact at all times? For some artificial intelligence (AI) enthusiasts, the Moorebot sounds like a dream come true. For the rest of us, it sounds super scary.

For some people, a small robotic device that can respond to directions, record memos, and snap pictures could be a great acquisition. While it remains to be seen exactly how this tool could integrate into the workplace (as it’s advertised), there are a couple of productivity red flags. Moorebot offers zero integration with other devices, meaning you’ll need to tuck it under your arm and take it on the train to retrieve your recorded notes after you leave work.

4. Slack

For some people, team chat and file-sharing software are among the most important collaborative tools of the 21st century. It allows some people to achieve #inboxzero on a near-regular basis. For others, chat software is a mess of distractions and a major annoyance. Chris J. Batts is one IT pro who openly loathes this innovation, stating it’s actually increased the volume of missed notifications, emails, cross-application effort, and general work in his life.

You may not be able to convince your boss to pull the plug on Slack, even if you’re in the same camp as Batts. However, if you feel like it’s a drain on your focus, you’re not alone. Some studies suggest it can take 25 minutes to resume productive work after a single interruption. If you’re stuck with real-time notifications, like the vast majority of us, the trick may be to turn down your volume, minimize the window, and check your messages on a periodic basis.

Some top productivity tools are game changers—others could just be massive distractions disguised as innovation. While some IT pros may find that the Phree and other tools allow them to get more done than ever before, most of us are likely to discover that keeping our devices and apps to a minimum is the wisest path.

4 tech trends driving the next industrial revolution

The Industrial Revolution changed everything about how people make products. Now, the assembly line is about to undergo a revolution of its own. It’s a change so dramatic it’s being called the Fourth Industrial Revolution—a transformation driven by tech trends, like advances in 3D printing, big data, and artificial intelligence. Here are four technologies driving that change:

1. The Internet of Things (IoT)

Computers have played a big role in manufacturing for decades (their arrival in factories was the Third Industrial Revolution), but they’re generally stand-alone devices; machines intended to help human operators complete specific tasks. The IoT is already starting to change this fact.

At the most basic level, it allows factories to proactively alert their operators of problems—or potential problems—in real time, notes The Globe and Mail. IoT essentially allows factories to operate as a single machine, but it goes further than that: IoT could also break down the barriers between processes and devices. “After the Fourth Industrial Revolution, there will no longer be a difference between information and materials, because products will be inextricably linked to ‘their’ information,” Markus Löffler, a senior partner at international management consulting firm McKinsey & Company, said.

In other words, information about an input—say a piece of metal or plastic—could be encoded directly on that input, allowing it to “know” where it’s going and what it will be a part of. Then, it’d share that information with the machines shaping and transporting it as it moves from the supplier through the manufacturing process to the consumer.

2. Artificial Intelligence (AI)

Connectivity isn’t the only defining feature of the Fourth Industrial Revolution. After all, allowing machines to talk to each other doesn’t really mean anything if they don’t know what to say. Artificial intelligence will organize, process, and make sense of the massive amounts of data generated by IoT devices.

And big data is just the beginning. Artificial intelligence allows for lean, or just-in-time, manufacturing to step up to the next level. A smart supply chain could incorporate data from retailers all the way through to suppliers. Imagine this: When you buy a product, the data gathered from that purchase is run through a predictive algorithm, and that algorithm incorporates forecasts based on sales of the product and other similar products.

Based on that, the system can predict when more of the product needs to enter production. It could even start that production and, because it’s directly linked to manufacturing facilities, know ahead of time when to order more inputs. This would all happen simultaneously and in a fraction of a second.

Most importantly, this technology already exists—it’s more a question of deploying it than developing it. AI won’t end there: UBS reports it could also lead to tech trends, such as extreme automation, where more tasks done by humans are done by machines. In manufacturing, that could equate to almost everything.

3. Autonomous vehicles

One of the first places we’re likely to see extreme automation is in autonomous vehicles. While no one knows exactly when self-driving cars will become common on public roads, it’s coming faster than many people believe. And, in factories, autonomous vehicles are already here. Autonomous and semiautonomous vehicles have been used in factories for several years, and it’s a tech trend that’s only going to continue.

BMW is currently testing autonomous robots that deliver parts to workers in its factories. ZDNet shared that Amazon uses thousands of autonomous vehicles in its warehouses to deliver entire shelves to employees, who then package the goods for shipping. Companies developing autonomous vehicles for industry are also attracting investment dollars from manufacturers.

Right now, this technology moves things around in factories, but as it develops, it could play a role in every stage of the supply chain—from shipping supplies to a manufacturing facility or delivering completed products to consumers.

4. 3D printing

Imagine an assembly line with just one machine. That’s the magic of 3D printing. 3D printing is poised to be one of the most important—and disruptive—technologies of this new industrial revolution. While other technologies will make existing manufacturing processes leaner, 3D printing could upend the entire manufacturing industry.

3D printing is already redefining the prototyping process. After all, 3D printing one item or 100 items costs the same, unlike with traditional manufacturing. One of the big innovations that led to the Second Industrial Revolution was the development of interchangeable parts. 3D printing has the potential to do something similar to the whole manufacturing process. A 3D printer can finish making one product and immediately start making something wildly different.

While the First Industrial Revolution standardized products, the Fourth Industrial Revolution will allow for endless customization. That opens up an interesting possibility—factories where every machine can make any product. From there, the next step could be the rise of Manufacturing as a Service—a future where product designers and developers buy manufacturing capacity as they need it in real time.

It’s an idea Gizmodo says is already being put into practice, albeit in a limited way. 3D printing could also allow for the decentralization of manufacturing. Instead of making a product at a factory and shipping it to a warehouse and then a retailer or consumer, 3D printing—and new supply chain efficiencies—could cut out that second stage.

It’s a future where consumers could order custom products online and have those products made almost immediately. Or it might come even closer than that. Ultimately, 3D printing could move the assembly line from the factory into our homes.

At the end of the day, these tech trends are worth watching. The average business shouldn’t jump on the bandwagon prematurely, but definitely prepare your IT environment today for the promising possibilities of new technology tomorrow.

Machine learning is here—are you prepared?

Have you noticed? We’re on the cusp of a new age—one of driverless cars and endless automation. It may still be years (even decades) away, but machine learning and other forms of artificial intelligence have already become a business necessity for many organizations. As IT managers, you need to be prepared.

Machine learning put to work

On the most basic level, machine learning is all about computers learning from the data they’re given and making predictions, or decisions, based on that. It’s a technology we all interact with—it’s what allows for the personalization of your Google search results, Facebook News Feed, and Amazon product recommendations.

That’s just the tip of the iceberg, though. This sort of technology is making an even bigger impact behind the scenes. More than 40 percent of executives at large companies say their organizations are already using machine learning in sales and marketing, according to a survey conducted by the Accenture Institute for High Performance. Expect that number to grow—76 percent of respondents to that survey said they think the technology will help them achieve higher sales growth. In addition, it’s being used to help veterinarians diagnose patients. It’s helping banks recognize customers when they call, and it’s being used to reduce vehicle accidents—and the associated costs.

Much of this adoption is being driven by the rise of the Internet of Things (IoT). All those connected devices are generating massive amounts of data—too much for even the largest team to go through—and much of that data is unstructured. Artificial intelligence allows you to make sense of that unstructured data.

A matter of quality

Still, implementing machine learning is a challenge. Companies need to know what business problems they’re hoping to solve, the causes of those problem, and what data they have around those causes. Without the right data, even the best AI can’t do much. That data also has to be good quality, or “clean.” Problems with data will lead to problems with results. Implementing this tech isn’t a fast process—these algorithms need to be trained, and that takes time.

Depending on the business problem the company is looking at, an off-the-shelf solution might be the answer. For others, there are a wide variety of APIs that can bring this sort of power to another program. IT managers will have to help their organizations navigate these challenges.

IT managers will also have to think about the infrastructure they’re using—AI systems tend to require a lot of computing power and the massive data sets that feed into them will have to be stored somewhere. For some organizations, the solution will be in the cloud. That means IT managers will have to deal with the same issues that come up when using any cloud-based service. With much of the data feeding into these systems coming from IoT devices, data security will also be a major concern.

While machine learning may replace human workers in the future, that’s still (mostly) the stuff of science fiction. The current reality is that this technology is more of an assistant, helping people make decisions based on data sets that are just too big for them to deal with. That said, preparedness is a virtue—especially when it comes to new technologies.