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Survey: mobile devices eclipse PC usage and, in a surprise, drive more conversions

Fluent poll of 2,773 US adults asked about a range of mobile usage patterns. Many industry insiders expect mobile commerce to eclipse PC-based sales in the relatively near future. A new online survey from Fluent argues that may already be starting to happen. The recent survey of 2,773 US adults affirmed other data showing that the bulk of consumers now spend much more time with their mobile devices than PCs. But the survey also reflected more transactions on smartphones than the PC — a surprise. Over the past year, which device would you say you spent the majority of your time using? According to Fluent’s data, the majority of these users’ online purchases are happening on mobile devices — by more than two to one. This is a seemingly contrarian finding that goes against the bulk of other data I’ve seen, which shows higher mobile traffic but lower (than PC) transactions. I asked Fluent to comment on this finding and they have yet to respond. At a minimum, this data can be seen as something of a leading indicator that mobile will overtake the PC for transactions; the only question is when. Among mobile transactions, slightly more were reported to have been made on apps than mobile sites, but just barely (51 to 49 percent). Slicing the data by age, gender and operating system yielded some interesting differences however: Women are more likely than men to shop on mobile websites Men make the majority of their smartphone purchases in apps Americans 18 – 34 are much more likely to shop on mobile apps; those over 45 are more likely to make purchases on mobile websites Android users are more likely to shop on mobile apps; iOS users on mobile websites Push marketing was also shown to be an effective driver of mobile transactions. Nearly 21 percent of Fluent survey respondents said that they had made a smartphone purchase after receiving a promotional email. This compares with 18 percent and 17 percent who did so after receiving a text or mobile push notification (respectively). According to third-party data, roughly three-fourths of email is now read on mobile phones. If corresponding landing pages and mobile sites aren’t optimized for mobile users the data above suggest that marketers and retailers are losing potentially meaningful revenue. In addition, these mobile users said that they would be more likely to shop on smartphones with “easier navigation” and “increased speed” and to some degree “enhanced security.” Older users were more interested in security and younger people were generally more interested in faster and simpler user experiences. The report offers a bunch of additional data about mobile app category usage and gaming. However the significant marketing takeaways are the following: Mobile commerce may overtake e-commerce on the PC sooner than we think Mobile app and site improvements can have a material impact on mobile transactions Push marketing on mobile devices can drive meaningful sales, with email being the most effective (and twith the added benefit of being cross-platform) Source:


What 2016 meant for Tech in Europe

Despite the slowdown of the US VC investments, 2016 was a tremendous year for our dearly beloved EU Tech Scene. For you, we analysed at the several thousand of funding rounds that occurred last year (according to Crunchbase Data), below is what you need to know: $11 Billion were invested in EU Tech startups, 45% (in amount) at a VC stage (round<$25M) 55% at a more PE stage (round >$25M) UK remains the leading ecosystem although is currently experiencing a slow down because of Brexit, especially on VC stage rounds France has overtaken Germany, in terms of VC funding. Looking at H2 2016, this trend is massive We strongly believe that the Tech EU boom will continue in 2017 Methodological note : we manually excluded every non-tech/digital companies and non-PE/VC/Seed/Angel rounds (which for instance represented several hundreds million $ just for France) As we anticipated in our post of the spring of 2016, the slowdown in the US had no bearing on European tech funding rounds. It’s like winter didn’t come for us :) Looking at the distribution of fundings among the main EU countries, here is what it looks like : In $ Billion But as the UK is a more mature ecosystem, with more large/PE rounds, to draw a comparison between countries we need to take only VC rounds (funding < $25M) into account. In $ Billion Although Britain is still leading, looking at the quarterly figures, the impact of Brexit is clear. France has slightly overtaken Germany because of a huge acceleration on H2 (+36% compared to H1). In $ Million Here is the distribution among the main EU Tech Hubs: In $ Million, excluding >$25M rounds So the year 2016 wasn’t so bad after all… But how about 2017? If we exclude the uncertain situation in the UK (and also the risks of politics outside Europe), all lights are green. Here is why : First and foremost (and what seems to me the very most important things), Tech startups are attractive for talent and many students want either to join a startup or to start their own business. First time Entrepreneurs are more and more skilled and experienced. They start looking at entrepreneurship early + education in school is more and more adapted for those who want to start their business. Money to support and to scale game changing ideas is here. Lots of new VC funds were raised this year both by new and existing teams. The tech investment business model has now been proven in Europe too, with lots of huge exits these last few months. Our own deal flow index -the Q1 and Q2 2017 next fundings- largely reflects this surge (please note that here it’s a 12 month moving average allowing us to remove seasonality) : For these few reasons why we strongly believe that the current entrepreneurial spring for tech in Europe will remain during the next quarters. Last but not least we wish you all an amazing year 2017, full of hope and self-fulfillment ! This article was co-written by Eric Gossart, Léa Verdillon, Sébastien Le Roy and Kevin Bonte from Serena Capital Team. Here is the slidehare of the study. Source:


5 Big Predictions for Artificial Intelligence in 2017

Expect to see better language understanding and an AI boom in China, among other things. Last year was huge for advancements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to. Positive reinforcement AlphaGo’s historic victory against one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the technique known as deep reinforcement learning. Reinforcement learning involves having a machine learn to solve a problem not through programming or explicit examples, but through experimentation combined with positive reinforcement. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how play the game at an expert level. The hope is that reinforcement learning will now prove useful in many real-world situations. And the recent release of several simulated environments should spur progress on the necessary algorithms by increasing the range of skills computers can acquire this way. In 2017, we are likely to see attempts to apply reinforcement learning to problems such as automated driving and industrial robotics. Google has already boasted of using deep reinforcement learning to make its data centers more efficient. But the approach remains experimental, and it still requires time-consuming simulation, so it’ll be interesting to see how effectively it can be deployed. Dueling neural networks At the banner AI academic gathering held recently in Barcelona, the Neural Information Processing Systems conference, much of the buzz was about a new machine-learning technique known as generative adversarial networks. Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. By working together, these networks can produce very realistic synthetic data. The approach could be used to generate video-game scenery, de-blur pixelated video footage, or apply stylistic changes to computer-generated designs.   Yoshua Bengio, one of the world’s leading experts on machine learning (and Goodfellow’s PhD advisor at the University of Montreal), said at NIPS that the approach is especially exciting because it offers a powerful way for computers to learn from unlabeled data—something many believe may hold the key to making computers a lot more intelligent in years to come. China’s AI boom This may also be the year in which China starts looking like a major player in the field of AI. The country’s tech industry is shifting away from copying Western companies, and it has identified AI and machine learning as the next big areas of innovation. China’s leading search company, Baidu, has had an AI-focused lab for some time, and it is reaping the rewards in terms of improvements in technologies such as voice recognition and natural language processing, as well as a better-optimized advertising business. Other players are now scrambling to catch up. Tencent, which offers the hugely successful mobile-first messaging and networking app WeChat, opened an AI lab last year, and the company was busy recruiting talent at NIPS. Didi, the ride-sharing giant that bought Uber’s Chinese operations earlier this year, is also building out a lab and reportedly working on its own driverless cars. Chinese investors are now pouring money into AI-focused startups, and the Chinese government has signaled a desire to see the country’s AI industry blossom, pledging to invest about $15 billion by 2018. Language learning Ask AI researchers what their next big target is, and they are likely to mention language. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively. This is a long-standing goal in artificial intelligence, and the prospect of computers communicating and interacting with us using language is a fascinating one. Better language understanding would make machines a whole lot more useful. But the challenge is a formidable one, given the complexity, subtlety, and power of language. Don’t expect to get into deep and meaningful conversation with your smartphone for a while. But some impressive inroads are being made, and you can expect further advances in this area in 2017. Backlash to the hype As well as genuine advances and exciting new applications, 2016 saw the hype surrounding artificial intelligence reach heady new heights. While many have faith in the underlying value of technologies being developed today, it’s hard to escape the feeling that the publicity surrounding AI is getting a little out of hand. Some AI researchers are evidently irritated. A launch party was organized during NIPS for a fake AI startup called Rocket AI, to highlight the growing mania and nonsense around real AI research. The deception wasn’t very convincing, but it was a fun way to draw attention to a genuine problem. One real problem is that hype inevitably leads to a sense of disappointment when big breakthroughs don’t happen, causing overvalued startups to fail and investment to dry up. Perhaps 2017 will feature some sort of backlash against the AI hype machine—and maybe that wouldn’t be such a bad thing. Source:


Gartner's Top Strategic Predictions for 2017 and Beyond

The firm's top 10 prognostications on where technology will take us include shopping in AR, corporate fitness programs, and much more. Every year at its annual Symposium conference, Gartner gives a list of its top 10 strategic predictions for the year ahead, and this year's list was dominated by the shift to conversational AI, augmented reality, and strategic modernization. Note these are specific predictions, rather than a list of important technologies going forward (which is a separate list that I'll cover in a later post). I always find these interesting, even if they don't all come to pass. Gartner Fellow Daryl Plummer, who delivered this year's list, said Gartner's predictions have been correct 78 percent of the time since 2010. (Here's last year's list.) Plummer said one thing tying all of this year's predictions together is the theme of "surviving the digital disruption." He said the scale of digital disruption is increasing and compared it with the destruction wrought by tornadoes and hurricanes: "They used to come out of nowhere; now they cover large areas over time." He said that "digital experience and engagement" will draw people into nonstop virtual interactions, and that business innovation will bring about an extraordinary departure from mundane concepts. In many cases "secondary effects" will be more disruptive than the initial digital change. In other words, when a disruption happens, it causes waves that in turn generate effects we didn't expect. To measure this, each disruption could be measured on a "digital disrupter scale" ranging from level 1, with things such as games, which are fun but don't have huge long-term impacts; to level 5, with things such as autonomous AI, which could have an enormous impact. "Instead of asking about jobs leaving the country, ask how many jobs will leave the planet," Plummer said. He noted that IBM will tell you that AI is not about replacing jobs, but about helping people, though he indicated he wasn't so sure about that. Here's this year's list: 1. By 2020, 100 million consumers will shop in augmented reality. Plummer said that AR games such as Pokémon Go are the beginning of the trend, and said that he believes organizations should start planning for AR shopping now. 2. By 2020, 30 percent of Web browsing sessions will be done without a screen. Because of the rise of "bots," to do many things, you don't need a screen, just to talk to the bot by voice for interaction. Plummer said the migration of applications will move from conversational user interfaces to bot interactions, then machine learning; for now, he urged attendees to think about "voice-first" solutions, beyond just customer service. By 2020, he said, the average person will have more conversations with bots than with their spouse. 3. 20 percent of brands will abandon their mobile apps by 2019. As more people start using bots and agents, Plummer said, fewer people will be using apps. Since people downloading apps and not using them sends a bad signal about a brand, hehe expects more companies will stop building them. He sees the migrations as being from the mobile Web to mobile apps to expired apps, but suggested that progressive Web apps might be a solution. Although right now the number of apps continues to rise, he expects by the end of next year, the number of branded apps will decline. 4. Algorithms will positively alter the behavior of billions of global workers. Plummer believes algorithms will impact 1 billion out of 3 billion workers, and that in general this will not mean replacing people, but instead influencing people to improve or become more efficient. Examples he gave included technology that helps pilots save fuel or helps bank employees to offer the right products. 5. By 2022, a blockchain-based business will be worth $10 billion. Blockchain offers the potential to eliminate costs in a system because it works with a shared ledger of transactions and does not require a particular trusted intermediary. This can work for financial transactions but isn't limited to such things. Plummer said he expects to see the deployment of multimode blockchains across industries by the end of 2017. Still, he noted that large blockchain communities in the business world are not yet built. 6. By 2021, 20 percent of all activities in which an individual engages will involve at least one of the top seven digital giants. Gartner defines these giants as being Google, Apple, Facebook, Amazon, Baidu, Alibaba, and Tencent, and believes that typical businesses will have the choice of joining with them or competing against them. Plummer expects that by 2018, everyone in the country will have at least two digital giant brands per kitchen. (I noted that Gartner doesn't consider Microsoft to be a "digital giant" even though Office 365 and Azure are popular services among its customers.) 7. Through 2018, every $1 that enterprises invest in innovation will require an additional $7 in core execution. This was one of the most intriguing predictions. For a long time, Gartner has talked about bimodal IT, with mode 1 representing the core, stable services and mode 2 representing the agile, fast-moving new ones. Plummer said that to make the new systems work, you much first modernize the core systems you have to unlock the data contained within them. He said the process is to modernize, then innovate, then transform—not the other way around. 8. Through 2020, IoT will increase data center storage demand by less than 3 percent. This is another somewhat contrary position. Gartner still believes there will be billions more endpoints in Internet of things (IoT) deployments. But Plummer said most IoT-generated data will not be stored or retained. This is operational data that will not go back to a central data center; instead, companies will analyze data in flight and sort out the small amount they need to retain. Most data, he said, will be generated in order to determine what to do next, and then discarded. 9. By 2022 the IoT will save consumers and business $1 trillion a year in maintenance, services, and consumables. Most of these savings will come through predictive maintenance, which he said saves 10 to 20 percent over preventative maintenance in most cases. He talked about the concept of a "digital twin"—for instance, a simulation of a specific plane and how that can be used to save money. 10. By 2020, 40 percent of employees can cut their healthcare costs by wearing a fitness tracker. Last year, Gartner predicted that by 2018, 2 million employees will be required to wear fitness trackers. It continues to believe that companies will offer corporate health monitoring, with employees who participate getting a discount on their health care costs. I'm skeptical. Plummer concluded by saying "the future is ours," and repeated what he called your mantra for the 21st Century: "Make It Digital, Make it Programmable, Make It Smart." Source:


5 predictions for artificial intelligence in 2017

Artificial intelligence (AI) has officially gone mainstream. Industry research firm Gartner named AI as its number one strategic technology for a second year in a row. The acquisitions race among giants like Google, IBM, Salesforce and Apple to purchase private AI companies keeps heating up — 2016 alone saw 40 AI-related acquisitions and our own research found that 62% of large enterprises will be using AI-technologies by 2018. Since everyone seems to be talking about AI broadly, we focused our predictions this year on what we see happening with communications and AI. See also: Will artificial intelligence mean the end of cyberthreats? As a leader in this area, we are working with enterprises to close the communication gap between man and machine. For 2017, our predictions are related to how we’ll communicate with computers and other devices, how AI systems will communicate with each other, and how we’ll communicate with each other about AI.   #1 – The movement towards conversational interfaces will accelerate The recent, combined efforts of a number of innovative tech giants point to a coming year when interacting with technology through conversation becomes the norm. Are conversational interfaces really a big deal? They’re game-changing. Since the advent of computers, we have been forced to speak the language of computers in order to communicate with them and now we’re teaching them to communicate in our language. Search engines like Google and Bing have already made big moves enabling search queries via spoken word while Facebook launched an AI-effort, DeepText, to understand individual users’ conversational patterns and interests. Meanwhile, the move toward natural language interfaces has already picked up steam with the explosion of companies focused on enabling chatbots, digital assistants and even messaging apps eclipsing social networks in monthly activity. Beyond 2017, think of a future when we can casually ask our personal devices for information regardless of subject – “How much money do I have in checking?”, “When was my last physical?” or “What restaurant within a 10-minute driving distance has an open table for 2 people?” #2 – Design will begin to evolve to increase our trust in AI If people don’t trust AI, they won’t use it. In the next year, designers will begin to apply knowledge of human interaction, specifically in the area of how we earn trust and respect, to AI systems. Elements of communication like tone, sentiment, timing, visual cues and word choice combined with AI technologies like natural language generation that increase transparency into how these systems operate will play a role in helping users trust and rely on AI systems. Stanford’s recent study on AI’s impact over the next 100 years states it well, “Design strategies that enhance the ability of humans to understand AI systems and decisions (such as explicitly explaining those decisions), and to participate in their use, may help build trust and prevent drastic failures, it’s critical that engineers and designers create systems that communicate freely about how they work.” In other words, if my AI-powered home monitoring system unlocks my home for an unscheduled visitor in the middle of the day, it better be able to explain why. #3 – We’ll start talking about how AI systems talk to each other In the next year, efforts will begin to create universal standards for AI to AI interactions. Without standards, AI technologies will increasingly become siloed or worse, interfere negatively with each other when multiple AI systems are involved in determining a single outcome. Imagine driverless cars on a collision course without the means to communicate with each other or an enterprise with multiple siloed AI systems that has a predictive analytics system moderating decisions about production levels but another AI system with a different data source that has indicated production needs to change. 2017 will be the beginning of talks among the tech giants, relevant industry associations and governmental bodies to establish universal AI standards. #4 – AI will take a hit due to imbedded bias In 2016, examples that reflected the multiple sources of bias that can occur within AI systems. Some of these sources include the data used to train systems, users’ interactions with the systems, similarity bias and the bias of conflicting goals. Most of this bias currently goes unnoticed but as AI usage grows and increasingly impacts people’s lives, recommendations need to be established for acknowledging and addressing systems’ biases or AI will take a major hit impeding future progress. #5 – Enterprises will start to demand ROI from their AI Companies will begin looking for demonstrable value and ROI proof points from AI technologies. While funding for AI-related startups keeps increasing – in the last 5 years alone, investments in AI have grown tenfold from $94M in 2011 to $1049M in 2016 – we’ve seen few real commercial applications surface. Most often these technologies are piloted by Innovation teams or an R&D department. 2017 will be the tipping point when companies start questioning their investments and AI will have to grow up. It’s pretty amazing to think that just two years ago we were talking about AI and robots coming to kill us. Tech luminaries were proclaiming AI would bring upon the apocalypse and now, some of these same people are founding organizations to push AI to its limits. So much has been accomplished in a short span of time, and we’re now starting to realize the benefits of partnering with AI versus fearing it. I’m already looking forward to next year when I can review my predictions to see how we fared – or more likely, I’ll be asking my intelligent system to tell me about my hits and misses. Hopefully, I’ll do well. Source: