Buying Twitter Followers is Sketchy. Could Artificial Intelligence be the (Legit) Alternative?

February 4th, 2019 & Filed under Blog

If you’re doing marketing for a smaller company, you’re very likely doing it all: juggling campaigns, content marketing, SEO, website updates, PR, social media, and whatever else comes along. You know your followers on platforms like Twitter are an important marketing opportunity, and you’re working hard to increase their numbers, in between all the other demands on your time.

photo of someone viewing Twitter on their iPhone

Fast tracking followers is a dilemma for any marketer, small business owner or social media manager. We’ve all heard how we can boost Twitter numbers by gaming the system through follow/unfollow bots or purchased Twitter accounts. Overnight, you’ve bought yourself a fan club and followers in the 1.5K range. Mission accomplished…if only it was actually worth anything.

The problem is most of those Twitter followers are just fake accounts. And dummy accounts don’t buy things, won’t engage, and will never make you much of an influencer. True, they can temporarily boost your street cred with inflated follower numbers, but if it’s a masquerade, you’ll eventually be discovered. And you can compromise your business’s integrity, because there are easy ways to check if your followers are real.

Buying followers also goes against Twitter’s terms of service, which could get you into hot water.

Earn Legit Followers with Some Help from AI

This is where artificial intelligence – or AI – could make a difference.

Building followers by sharing relevant content is the traditional, widely-recommended (and legitimate) way of increasing your audience. You share something compelling, and your followers help boost your numbers by liking, sharing or commenting about it to their networks. But the organic nature of this takes time and effort. Could AI speed things up?

AI is known for processing large sets of data, which can help us understand a broad sense of what people are interested in, like a satellite map gives us a big picture of a certain geography. Twitter’s categories can capture sweeping interests like sports or technology. But Twitter is a platform made up of individuals. Each of us, as unique tweeters, want to read things geared to our specific interests.

What if you could use AI to zero in on small chunks of data – as small as a single tweet – to understand what your followers are actually interested in, and share relevant content, on a regular basis, to each of those individual followers? Your followers would be much more likely to ‘like’, retweet, and reply.

Increase Engagement Through Individualization

Primal has an AI solution that can look at a tweet from one of your followers and understand the meaning and context of the post. It can then find and recommend content based on what that person tweeted. This means when one of your followers tweets something related to your brand, Primal can find a relevant article and tweet it back in response.

For example, if your follower tweeted “Friday looks like a great day to head out on the boat and catch some big ones”, Primal’s AI would understand how ‘boat’ is related to ‘catch’ and infer that your follower has a specific interest in fishing. It automatically sends a unique Twitter reply from you to that individual follower with a relevant article about fishing.

Now you’re micro-targeting content, continually engaging your followers, and responding to their unique interests. That’s the key to individualized marketing. And using AI means you’re saving tons of time and effort.

Amaze, Amplify and Influence

Your followers on Twitter want to be understood as individuals. Primal can provide individualized engagement at scale, creating authentic conversations, increasing loyalty, and organically growing your audience.


Want to see how AI can help you grow your Twitter followers? Try out Primal’s Intelligent Assistant for Twitter

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How Small Data Helps Understand Individuals in Real Time

November 14th, 2018 & Filed under Contributed Article

This article originally appeared in Inside Big Data


In this special guest feature, Jeff McDowell, COO of Primal, discusses the importance of small data and big data analysis when providing timely and accurate AI recommendations based on interest compared to the stale irrelevant recommendations most online channels provide today. Primal is a leading AI research company that builds knowledge graphs in real time, allowing companies to better understand meaning even in small data environments. Jeff has a proven track record of driving strategic growth through a combination of non-linear insight and practical execution. Prior to joining Primal, Jeff is best known for leading the global alliances and enterprise marketing teams at BlackBerry, and the marketing and business development team at Desire2Learn.

There’s no question that companies have benefited from the rise of big data AI solutions, through an exponentially increased understanding of their customers’ behaviors. But is big data always the right approach for gleaning insights about individual customers interests?

It turns out big data approaches have some constraints, not the least of which is that they tend to have a limited ability to understand the fluidity of interests at an individual level. If someone was interested in running a year ago, and playing poker a few months ago, but is now no longer interested in either topic, a statistical approach using big data won’t be able to pick up on these nuanced changes.

As customers begin to demand more individualization of services, companies need to be able to understand them at this specific level and also keep up with how these interests may change over time. The answer lies in small data AI.

Does Your AI Really Understand Your Customers?

Most AI solutions today don’t take small data into account. Let’s take Twitter for example. Twitter attempts to understand its users by analyzing all the data they’ve collected to infer user’s interests and categorize them into static buckets. A year ago someone may have tweeted a few times about knitting (a passing interest of theirs) and Twitter put them in the broad interest category of “knitting”. Now a year later, they’re still included in the knitting interest category and advertisers are sending promotions about yarn. This is annoying for the user and a waste of money for the advertiser.

Try this for yourself by looking at your Twitter settings and click on “Your Twitter Data”. Scroll to the bottom to see a listing of the interest categories that Twitter has placed you in. If you’ve been on Twitter for awhile, this list will certainly include some outdated topics that no longer apply to you. You might also notice some “interests” that are way off. Twitter gives users the ability to remove themselves from any irrelevant or outdated categories, but how many people regularly review this, if ever?

Small Data AI to the Rescue

A real-time small data AI approach would solve Twitter’s ad targeting problem by looking at what individuals are interested in now rather than the categories they may have been placed into months or years ago. Small data analysis can read a very small piece of text (like a tweet) and use AI to understand the meaning and context of the words. Then it can build a knowledge graph around the post to expand upon the understanding of the interest. This provides a richer insight than keyword targeting, which only searches for matches to a very specific keyword or phrase making it much more limited than an AI approach.

As an example, a vendor of fly fishing rods might want to target people who tweet things like: “Catching lots of trout in the Saugeen River!” This tweet doesn’t use the keyword “fishing” but the expanded knowledge graph built by AI would understand that trout is fish, and a method for catching trout in a river is fly fishing.

All of this happens in real-time, so a small data AI solution is able to understand interests as they evolve. In our Twitter example, this approach would mean a user’s current tweets drive the type of advertisements recommended to them, rather than the approach of targeting using static interest categories. This ensures ads are highly relevant to what users are interested in right now – a win for both users and advertisers.

Could Small Data Work For You?

Of course, not every company is Twitter, but there are many other examples of small data – chat sessions with customer service representatives, comments to a blog post, or product reviews – that can be analyzed with AI to gain deeper insight into customers.

Small data isn’t a replacement for big data. An AI solution needs to analyze both if companies want to have a complete view of their customers at both an aggregate and an individual level.

Small Data or Big Data – Which Matters Most for AI?

August 27th, 2018 & Filed under Blog

By Jeff McDowell, COO at Primal

In the past year, we have seen countless headlines about how artificial intelligence (AI) will transform business. AI promises to provide insight into data and customers at a level of individualization never seen before. In response, many companies are scrambling to capture and store as much data as possible – but in doing so they might be increasing their exposure to data breaches, privacy violations, and hacks.

Unfortunately, by taking a standard “machine learning only” approach to AI, we may not get far out of the starting blocks to achieve the vision of an AI solution that can understand data at a high level of fidelity. Many people assume that storing and analyzing large amounts of information (“big data”) through machine learning is the only way to take advantage of AI. But machine learning approaches can be actually be ineffective in understanding the meaning of text or the interests of individuals with any sort of specificity. Any company serious about AI needs to develop a solution that is both more targeted and more secure. I believe the way forward lies in integrating small data analysis into a big data approach.

Here are a few reasons to consider small data:

Big data techniques can be expensive and ineffective at high levels of specificity: Just like satellite imagery provides a broad picture of geospatial data of a physical lake, today’s big data approaches do the same with data lakes. When statistical methods of AI are applied to a big data environment, the output is usually very generalized and lacks fidelity. For example, if a statistical model is looking at data about sports fans, it may see a pattern that groups people into categories such as “baseball enthusiast”, “football enthusiast”, etc. These broad categories lose sight of the fact that some users are actually a pitching enthusiast, or a statistics junkie, or a part-time umpire. Knowledge of these narrower topics would be extremely useful to advertisers of niche products, yet big data platforms today are very limited in identifying and exposing these higher fidelity interest categories. This is because processing and storage becomes increasingly more expensive and complex when analyzing large amounts of data to achieve higher levels of specificity.

Integrating small data analysis is the key to making AI meaningful: Small data simply refers to the quantity of data available to train models. It’s often defined as the amount of information that can be processed by one computer, but it could be even smaller than that – a spreadsheet, a document, an article, or even as small as a social media post. “Small data” can even be found within large data sets. Instead of applying statistical collaborative filtering techniques to a group of people to infer broad interests which are hit and miss, taking an approach that applies semantic or symbolic techniques to small data can look at an individual to understand exactly what they are interested in, no matter the level of specificity. For our baseball example, a small data approach would analyze the meaning and context of a person’s blog or social media post, and pick up the nuance between someone who likes statistics vs someone who is interested in pitching techniques.  

Small data approaches increase explainability and reduce potential for bias: One of the criticisms of AI is that it operates in a “black box”, where it can be difficult to determine the reasoning behind a specific output. Numerous organizations – including the National Institute of Standards and Technology (NIST) – have called for a more balanced and thoughtful approach to developing AI solutions, to ensure they are trustworthy and explainable. AI outputs based on small data are inherently easier to interpret by humans. AI systems which analyze and categorize users based on large data sets also have the risk of introducing biases over time – a problem that can be mitigated by integrating analysis of small data, which can serve as a self-correction against bias.

AI has huge potential to augment our human intelligence and make us more productive. The importance and power of small data for AI is still on the fringes of being understood, but will gain momentum as businesses and consumers increasingly expect a greater level of relevance and security from AI. Even Eric Schmidt, former CEO of Google, recently tweeted, “AI may usher in the era of ‘small data’ – smarter systems can learn with less to train on.”

The current model of statistical analysis of big data is ‘good enough’ for now, but not sustainable. For AI to really be relevant, efficient, and safe, big data must be balanced by a robust small data processing activity.

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How your employees can – and must – protect intellectual property

July 31st, 2018 & Filed under Contributed Article, News

This article originally appeared in the Globe & Mail Leadership Lab

by: Jeff McDowell, COO at Primal

The current economic uncertainty is nothing compared to the financial crisis of 10 years ago. Then, not many companies or consumers were considering the potential of artificial intelligence. In fact, AI was still somewhat seen as science fiction. As an AI company that endured that economic uncertainty, Primal has a unique perspective on how to motivate your work force to innovate to strengthen your business and the Canadian economy, independent of the global environment.


While no business can claim immunity from the challenges economic uncertainty brings, we believe there are a few strategies that can enable a business to innovate during these periods. The most important is establishing a compelling vision for what unique business problems your company is attempting to solve. The intellectual challenge of accomplishing something no one else has done before will motivate employees.

Businesses must have conviction about achieving your mission, no matter the external factors that may threaten to take you off-course, be it trade headwinds or the latest buzz-worthy AI tool.

We didn’t begin 12 years ago with a vision of being an AI company. We were focused on a specific problem – how to use technology to find meaning contained within small data. This involved complex data science and research that had never been focused on before. We had a strong nucleus of innovators that stayed true to this vision, and several years ago, it became clear that the emerging AI market was shifting toward us. That validated our mission and made us more focused.

Solving a major AI problem is like landing on the moon. There’s lots of risk, but incredible payoff in being the first one to do it. While some larger companies may provide financial incentives for patent discovery to encourage the discipline of protecting intellectual property (IP), the unique problem that our company was focused on was more than enough to keep our work force passionate about finding the solution.


To stay true to your mission, you can’t be a trend-chaser; you need to be thoughtful about integrating new technologies into your business. At a recent conference I attended, a panelist made reference to companies “sprinkling some AI on that” as a strategy − and although it was said in jest, sadly too often this is true (and not just with AI).

As with all things in business, the advantage comes not from AI technology itself, but from deeply understanding the mechanics of your company and how you will benefit from the deployment of new technologies.

For some companies, AI may fit best within their customer-service function − but that doesn’t mean you should just add chatbots to your website. We’re working on a case where AI acts as a virtual assistant, monitoring a chat between a customer and a service agent and suggesting highly relevant resources in real-time to the service agent as new questions arise. This allows the agent to remain empathic and engaged in the conversation, rather than furiously searching a knowledge base for prescriptive answers that may be hit or miss.


Unlike revenue, IP is an asset that is less impacted by short-term economic fluctuations and can help buffer your business to withstand times of economic uncertainty. Primal has 148 international patents, but as we built our company, we didn’t motivate our employees by setting a patent goal to achieve. We established a mindset of evaluating each new aspect of a solution to determine if the idea was new, core to our technology and worth protecting. If so, we patented it, and this led to an extensive portfolio. By protecting IP, it bolstered employee conviction that we were onto something noteworthy and solidified their commitment to the vision.

One of the strengths of Canada’s economy is the fact that we have so many innovative homegrown AI companies. As the global AI race speeds up in parallel to increasing economic uncertainty, the need to keep this valuable IP protected is imperative.

Unfortunately, protecting IP is an area where Canada lags globally. Only 10 per cent of small and medium-sized businesses in Canada have IP, and only 9 per cent have IP strategies.

The Canadian government is helping reverse this trend with its new national IP strategy, which supports local innovators through increased resources and legislation. But it’s Canadian companies ourselves who need to see the value in protecting IP − to keep our employees motivated and validate their innovations, to protect our businesses’ hard-won knowledge and to keep strong companies growing and thriving in the Canadian economy.



Primal Receives $2.3 Million Financing from BDC

May 16th, 2018 & Filed under News, Press Releases

With its artificial intelligence platform backed by 148 international patents, Primal is accelerating commercial momentum with BDC support

WATERLOO – MAY 16, 2018 – (BUSINESS WIRE) — Primal, an artificial intelligence (AI) company powering the next generation of consumer and enterprise applications that augment human intelligence, today announced that it has received a $2.3 million loan from BDC.

With over $25 million in private funding invested in R&D, Primal has built one of Canada’s most significant international AI patent portfolios with 148 patents. Primal will use the financing to commercialize new applications, pursue strategic partnerships, sign licensing deals, and invest in sales and marketing to drive rapid adoption of Primal’s technology.

“The Canadian AI landscape is growing rapidly and it is becoming increasingly important to deliver real value to businesses, consumers, and governments,” said Pierre Dubreuil, EVP, Financing at BDC. “Primal’s approach has great potential to contribute to Canada’s position as a global AI leader and this financing will help them along this path.”

“AI is not about replacing humans but about making humans smarter, better, faster,” said Yvan Couture, President & CEO of Primal. “We are excited to receive this support from BDC as it will help us bring AI to life in meaningful ways; shaping the future of how people perform in business and everyday life.”

Primal’s augmented intelligence platform uses advanced semantic synthesis and knowledge representation to understand text in a similar manner to how the human brain processes and represents information. Differentiated from common machine learning techniques, Primal does not require large amounts of training data to understand meaning, which allows it to address small data environments where more traditional approaches don’t work.

“Applications that can use this level of personalization are endless,” said Jeff McDowell, COO of Primal. “It’s like having a conversation with a friend who knows you well. With small amounts of information, a friend can make highly relevant recommendations to you by expanding their thoughts within the context of your likes and interests.”

About Primal:

Primal is an artificial intelligence (AI) company powering the next generation of consumer and enterprise applications that augment human intelligence. With over $25 million in private funding invested in R&D, Primal has built one of Canada’s most significant international AI patent portfolios with 148 patents. Primal’s augmented intelligence platform combines advanced semantic synthesis and knowledge representation to understand text in a similar manner to how the human brain processes and represents information. To stay up to date with Primal, visit and follow @Primal on Twitter.

About BDC:

BDC is the only bank devoted exclusively to entrepreneurs. It promotes Canadian entrepreneurship with a focus on small and medium-sized businesses. With its 118 business centres from coast to coast, BDC provides businesses in all industries with financing and advisory services. Its investment arm, BDC Capital, offers equity, venture capital and flexible growth and transition capital solutions. BDC is also the first financial institution in Canada to receive B Corp certification. To find out more, visit


Media Contact:

Kellen Davison

Forecast PR


Bringing AI to its full potential with augmented intelligence

May 14th, 2018 & Filed under Augmented Intelligence, Use Cases

It’s not even halfway through 2018 and the buzz around artificial intelligence (AI) is bigger than ever. It seems we’re finally starting to get past the overblown fear about a robot-dominated future, but the fear has been replaced by a lot of hype. AI is now being thrown around as a buzzword in every part of our lives.

If everything is AI, what exactly is AI anyway?

Like many others – including IBM – we think “augmented intelligence” is a better way to describe what we do with our AI platform. Primal’s augmented intelligence applications help people do their jobs better, serve individuals’ interests, and bring AI to its full potential. Unlike the fears of AI taking over everyone’s jobs, this kind of AI (augmented intelligence) is not a replacement for people – it enhances what people can do with their limited time and resources. The nail gun didn’t displace the carpenter, but carpenters sure are happy they have nail guns.

One of the use cases of Primal’s AI platform is an augmented intelligence application that can determine the meaning of content in order to find other content which is highly related. Primal does this much like a real human brain by using semantic synthesis (where semantics refers to the meaning of words, and to synthesize is to combining several things into a coherent whole).

Primal’s AI breaks textual objects such as an article, a post, a tweet, etc. down to an ‘atomic’ level. As an overly simplified example, “firetruck” becomes “fire” + “truck” and finds meaning by analyzing how this combination of words has been used across millions of online sources. It then synthesizes the meaning of each word (semantics) to determine the message being expressed in the original content.

In our firetruck example, the word “fire” could be related to wood, burning, and emergency, but also camping, coziness, and marshmallows. Only some of these words would be used in the same context as “truck”. Combining the words “fire” and “truck” together would net more relevant results such as burning, emergency, rescue, fire fighter, arson, etc. Again, this example used the simple term “firetruck”, but Primal has the ability to analyze and synthesize the meaning of a larger piece of content such as an entire news article.

Once Primal has analyzed the message of the content in this way, it can find and recommend other content that has the same meaning – not just similar keywords. All of this happens on-the-fly in real time, with each request to Primal creating a new set of potential connections and recommendations. Unlike big-data AI solutions which require lots of training data, Primal can find meaning from very small pieces of data, like a tweet.

Because it doesn’t rely on specialized databases, Primal’s technology can be used in a variety of different industries and use cases.

If you’re a social media manager, you know that curating and selecting relevant content to share on social channels is time-consuming, and it’s hard to break through the noise to get noticed by your target audience.

Primal’s augmented intelligence tools for social media can send individualized content to each person who follows you – not just random content, but highly relevant articles that fit in the overlap between their interests and those of your brand. Articles could be from third-party sources or from your own content library – enabling greater usage of created content.

When your followers receive an article just for them, it stimulates much greater interaction than when they read a generic post sent to your whole channel. Often it sparks a conversation, providing an opportunity to connect in new ways and build brand loyalty. They’re more likely to share the content further, which extends the reach of the original post and ultimately results in new organic followers for you. And all of this can happen automatically, freeing up your time and enabling you to connect to many more people.

Primal isn’t just for recommending articles or other content – it can also analyze product descriptions. When you run an online store, the product recommendations your customers see are usually based on what other customers bought. But it takes a lot of user data to teach the system to make meaningful recommendations.

If you’re not Amazon and you don’t have millions of customers, how do you get enough data to populate your recommendation database? With Primal’s augmented intelligence, you don’t need lots of data – Primal can analyze the original product description to determine meaning and recommend other highly relevant, complementary products.

These are just a few use cases where augmented intelligence can help you better engage with your customers, provide extra value, and increase your productivity. How could this work for you? Let us know by dropping a line to

Communitech @20: Back where it all began

June 5th, 2017 & Filed under Team

Waterloo Region, September 29, 1993. It’s early morning when local entrepreneur Yvan Couture begins pitching an idea to a crowd gathered at the University of Waterloo’s William G. Davis Centre for the monthly meeting of the Computer Technology Network.

Couture, then founder, President and CEO of Taaz Corporation, a consulting firm serving the tech community in southwestern Ontario, is jazzed about the growth opportunities he’d seen industry associations provide to techies in places like Ottawa and Raleigh, North Carolina – peer-to-peer training, mentoring, conferences – and has built out a similar plan for Waterloo with the help of Ruth Songhurst, then president of Mortice Kern Systems (MKS), a local software firm.

Using appliqués on an overhead projector, he begins to tease out a vision for a member-funded organization that would “provide the vehicle for assisting the local technology sector to prosper and become the main driving force in the local economy, as well as enhancing Waterloo Region as a hotbed for world-class technology.”

The roomful of entrepreneurs is more interested in the free doughnuts and coffee than anything Couture has to say.

“It fell very flat,” Couture says with a laugh. “One of the key guys was adamant that there was no way that anybody would pay, so it kind of died. It didn’t go anywhere.”

Well, not yet.

Read the full article

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The Social Graph Must Die

May 3rd, 2017 & Filed under Featured, Industry Analysis

Have we reached the breaking point? Has our collective exhaustion with social media — the incessant notifications, the oppressive filter bubbles, the outright denial of our individuality — finally exhausted the excuses, apologies, and band-aid fixes?

Here’s the #ElephantInTheRoom: The social graph is a terrible basis for matching people to information. This is probably not a message that will be welcomed by the corporate giants and countless ventures that are banking on the social graph, but it’s an argument that’s long overdue. The social graph must die.

To be clear, the social graph is not social media. The former is the model or representation of the relationships between people. It’s the foundation upon which social media services match people to information. And in this capacity, the social graph is irredeemable.

Read the full post on Medium

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Are Machines Stealing Your Job?

October 8th, 2016 & Filed under Industry Analysis

Three myths about artificial intelligence and its impact on content creators.

Industrialization is transforming our information economy, destroying old business models and creating new opportunities. The impact it will have on content professionals will make social media seem tame in comparison. To understand this transformation and leverage it to your advantage, you need to parse the myths from reality.

Read the full post on Medium

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Primal CTO Ihab Ilyas Awarded Thomson Reuters-funded Research Chair

September 29th, 2016 & Filed under News, Team

Thomson Reuters and the University of Waterloo are joining forces to fuel breakthroughs in data science and develop the next generation of global entrepreneurial leaders. The wide-ranging collaboration is valued at over CAD $20 million over the next five years.

Their collaboration includes the establishment of the Thomson Reuters-funded Research Chair in Data Cleaning from Theory to Practice. The chair is initially held by Professor Ihab F. Ilyas from the Cheriton School of Computer Science at the University of Waterloo, whose research is investigating new methods for storing, cleaning, and curating data. In this role, Professor Ilyas will continue focusing on integrating and curating data in an effort to overcome the problem of data silos, and help businesses make better use of their data.

Read more at PR Newswire

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Azure-Backed Primal Delivers Personalized Content via Bing Search APIs

September 29th, 2016 & Filed under News, Product Updates

Originally published on MSDN Channel 9:

Primal is the maker of a web-scale network of personal intelligent assistants for content discovery. Primal uses Bing Search APIs in the Azure Marketplace to deliver highly personal content at web scale. The Bing knowledge and intelligence ecosystem complements Primal’s internal AI development. Primal’s AI formulates highly specific queries across any domain, and Bing Search APIs retrieve highly relevant content that satisfies its customers’ unique interests.

“Primal’s patented AI provides a comprehensive understanding of consumer interests. Bing Search APIs give us comparable reach with our content supply. Together, Primal and Microsoft Azure deliver personalized content at web scale,” said Peter Sweeney, Founder & CSO, Primal.

To learn more about how easy it is to add Primal’s personalized content discovery to your apps, download the datasheet and the mini-case study.

To start exploring your own Primal-powered solution, check out our Developers site.

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Automated Content Curation Your Readers Will Love: A WordPress Plugin That Uses Your Interest Graph

September 19th, 2016 & Filed under Product Updates

Engage your readers with great content that expresses your interests!

Primal for WordPress uses Primal’s powerful interest graph and content filtering technology to give your readers relevant and timely content that’s tailored to each individual page you create.

Read more »

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The Industrialization of the Internet

September 18th, 2016 & Filed under Industry Analysis

The industrialization of the internet is driven by the relentless pursuit of productivity advantages, not quality improvements.

“The digital revolution is probably going to be as important and transformative as the industrial revolution.” — Ryan Avent, The Economist columnist, author of The Wealth of Humans (via The Atlantic).

Web 2.0 was a social revolution: many hands make light work. In stark contrast, the current revolution, powered by artificial intelligence and machine learning, is industrial: the automation of tasks displaces human work. But trite definitions don’t prepare us for change. Whatever you call it, our digital economy is in the midst of profound changes. By placing these changes within the frame of industrial manufacturing, the true values and motivations that underlie them than are illuminated.

Read the full post on Medium
Read more »

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The History of the Semantic Web is the Future of Intelligent Assistants

August 19th, 2016 & Filed under Industry Analysis

The Semantic Web provides an enticing vision of our online future. This next-generation Web will enable intelligent computer assistants to work autonomously on our behalf: scheduling our appointments, doing our shopping, finding the information we need, and connecting us with like-minded individuals.

Unfortunately, the Semantic Web is also a vision that, to some, seems very distant, perhaps even outdated. It has been over a decade since it was popularized in a May 2001 article in Scientific American. Semantic Web researchers and engineers have been toiling even longer on the monumental technical and sociological challenges inherent in creating a global Semantic Web.

The good news is that we are seeing evidence today of its accelerating emergence.

Read the full post on Medium

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Goodbye .@, Hello Messaging?

May 31st, 2016 & Filed under Industry Analysis

A single-character change in how tweets are distributed highlights a massive strategic decision for Twitter.

When I first started using Twitter, I’d start my tweets with the names of other users. I wanted to give them credit for their ideas. I later discovered very few people even saw my tweets because of how they were composed. It was frustrating (and a little embarrassing) to learn I didn’t know how to use the service.

Read the full post on Medium

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