Posted on May 30, 2017
For decades, humanity envisioned and dreamed of a technology-enabled future. One with autonomous transportation, flying vehicles, a clean and safe environment and a healthy, extended life.
Today, more than 60 years after John McCarthy coined the term “artificial intelligence” to describe the science and engineering of making machines intelligent, and after surviving two so-called “AI winters,” recent advancements suggest that the once-distant future of our dreams is becoming a reality.
Our dreams, however, only scratched the surface of what we now believe will be possible throughout the coming years.
In recent years, AI bested its human counterpart in the most strategic of games, including Jeopardy and Go. Just recently, AI has acquired the skill to handle mis-information and incomplete information by winning against world-class poker players in a Texas’hold’em contest. Although Artificial General Intelligence (machines that compare to or surpass the human mind) still belongs in the distant future, researchers believe that machines are gradually approaching human levels when performing “simple” (tasks that are simple for humans, not machines) tasks, such as understanding naturally spoken language or evaluating unknown, new situations (in non predictable environments).
In fact, one of the most common applications of AI today is speech recognition. Personal virtual assistants like Alexa, Siri, Cortana and Google Assistant can understand speech and respond to it accordingly. The biggest breakthrough in speech recognition thus far has come from IBM, which has managed to reduce the error rate in conversational speech recognition to 5.5% (relative to the human error rate is of 5.1%).
Other existing AI applications include predictive technologies found in early self-driving cars and search engines. Companies such as Netflix and Pandora are also using AI to improve content recommendations.
If an AI-future is inevitable, then we must identify and study the paradigms of applying AI in the coming years.
It’s realistic to envision an AI-human hybrid that increases our mental skills and masters scientific challenges. This hybrid may also extend to combining our bodies with artificial devices that enable us to improve our physical or cognitive abilities.
Despite countless advancements, machines still lack the ability to process deep emotional intelligence. In response, much research is being conducted and time spent training AI to read a user’s emotional needs. Although these machines cannot fully understand emotion, businesses are now implementing cognitive technology tools in the form of bots and virtual agents to handle customer questions. These technologies can detect various emotions and develop customized responses to offer more empathetic feedback and support. We have finally reached a point where humans and machines can build engaging relationships, thus allowing businesses to provide more personalized services through AI.
This is the starting point for the three core paradigms that will shape the applications of AI technology in the coming years.
The internet enables us to connect, share and engage without time, location or other physical constraints. And now, bots are poised to change humankind’s favorite communication technology: messaging apps. However, while conversational interfaces allow us to engage chatbots, the technology still lacks the broad understanding of individual conversational context to create a meaningful and valuable interaction with the user.
However, Gartner predicts that conversational AI, when used properly with visual solutions and UX, will supersede today’s cloud and mobile-first paradigm by 2021.
Although conversational AI is primarily deployed in customer- or user-facing applications, I expect a much bigger use in bot-to-bot communication across business applications throughout the next 2 to 3 years. This type of bot will result in a true personal assistant.
Bot-to-bot communication will be the most used form of interaction involving bots. It will extract the real value from these systems, providing access to information sets that couldn’t be provided by user-facing interactions, and even less by competitive services at scale in the past.
Mass individualization will change the way products are made. New offerings will automatically be created based on an individual’s current and future needs. Web applications will serve and produce specific items on the fly, via bot-to-bot communication and real-time customer engagement. This trend will lead us to a world where machines are deeply integrated into our everyday lives.
How? Mass individualization is poised to take over today’s mode of mass standardization,and enable entrepreneurs and product managers to build offerings around each user’s personal context. This will include factors such as their behavior, attitude, goals and needs — all understood via conversational (message-based) applications, buying and browsing history, geography and much more.
Early progress in content and e-commerce will push the digitization of industries to new heights thanks to its ability to transfer complex processes and engagement models that usually require a large amount of service. High-cost, repetitive processes are poised for disruption, such as accounting and legal advice. AI will help professional augment their work, acomodate the customized needs of more customers and introduce entirely new business processes that increase efficiency and productivity.
By definition, technological convergence is the tendency that different technological systems will evolve towards performing similar tasks. New technologies take over to perform the same task but in a more advanced manner.
AI-enabled convergence means that AI-based technologies are embedded in all new systems which provide smart, context-aware and pro-active products and services that can engage with humans in a more natural and smarter way. These systems can either be based purely on software applications or on robotics that engage with us physically.
AI will be used together with enabling technologies such as Blockchain, a distributed but controlled network of billions of systems connecting and interacting with each other. Or IoT, which allows systems to collect, send and receive information about a product or device’s environment, condition and performance. It will also be used with other technologies like VR/AR, 3D printing, autonomous robotics, renewable energy sources and advanced genomics like Next Generation Sequencing (NGS). The results will transform entire industries, our everyday lives and our socioeconomic existence.
These three core paradigms are going to shape the way we make, use and engage with machines in the next few years — and more advancements are expected soon.
These paradigms will guide society in handles our corporate, consumer and individual relationships with technology.
For now, we must seek new opportunities while remaining diligent in how we train AI. New systems will only be as responsible as they’re trained to be. As we continuously seek to develop intelligence AI, we hope that it will ultimately provide society with the tools that make everyday life easier, and the world operate a lot better.
Posted on May 14, 2017
Posted on April 24, 2017
Posted on March 2, 2017
The landscape of corporate venture capital in 2017
Corporate investment has increased radically in the past decade, with total corporate venture capital (CVC) investments growing more than 500% between 2009 and 2015, propelled by the rise of social media and mobile technology in contemporary markets. Unsurprisingly, most CVC dollars are put into the technology sector, which accounted for 63% of CVC activity in 2016. Tech companies, it seems, are devoting their profits to invest in smaller companies’ research and innovation. As Forbes Magazine notes: “It’s a strategy that allows [large corporations] to test ideas at immense scale — Google Ventures, for instance, the largest corporate venture investor in the world has invested in more than 300 startups— and discover synergies with their existing businesses.” Also, other sectors are also benefiting from corporate investment. Healthcare is climbing rapidly as a share of CVC investment dollars, and worthwhile market opportunities still exist in consumer as well as retail.
Moreover, CVC investment has become smarter. There are more sophisticated investors with a history investing in venture capital; they have learned more about what works and what doesn’t work. Corporations are beginning to understand startup culture and embrace open innovation: two contemporary modes of business that appear at odds with traditional corporate culture. Furthermore, they have learned how to work strategically and actively, taking a specific corporation’s strengths and adding to them, picking startups that are more in tune with a corporation’s assets.
The core problems that develop when creating CVCs
However, there is still a long way to go to effect maximum success in corporate venture capital. If done well, CVCs can yield long-term benefits and assets—the issue is that they are not often done well. While of course corporate investment projects have had varying levels of success, most CVCs have run up against a specific set of problems.
1. Often CVCs are not independent from their corporations usual business practices, then they won’t be able to adapt to the market nor could they act on their long-term vision. Being tied to business units in regards to the investment decision making process usually limits the CVC’s ability to act quickly and negotiate market terms when co-investing with other VCs. Being dependent on corporate management from a financial perspective is another risk that doesn’t allow a CVC to position it as a reliable investor. Not only does such a dependency create tensions around a CVC’s long-term goals and their partners executing on them, but can even more lead to conflicts at an investment’s board level and misalignment in exit scenarios.
2. CVCs are often non transparent and inconsistent. Due to a direct or operational link to business units, CVC activities tend to be influenced by quarterly or annual operational changes. Besides, the change of responsible teams leads to problems like missing trust and commitment in portfolio management and co-investor relations with other VCs. Most importantly, the lack in a dedicated leadership team driving the CVC’s strategy and deal closings makes decision making processes inconsistent and non transparent.
3. Often CVCs are inexperienced and without a long-term vision. If a CVC does not operate separate from corporate management, it will operate at cross-purposes. Investing venture capital is a full-time role, requiring a team’s full attention, commitment and responsibility. Similarly, it cannot primarily be a marketing and sales operation either. A CVC will, of course, be accountable to the business’s larger concerns, but it needs to be detached from the company’s other operations. Business units can be more interested in quarterly results than long-term investment cycles. From outside of the CVC, such corporate investment operations look unpredictable.
Creating a real CVC for long-term success
1. Aim for longevity from the start: The CVC must be imagined as a long-term entity; in other words, it must be flexible and able to adjust focus, while still holding true to is core goals. In this way, the CVC needs to use the advantage that the larger corporation provides. By using the corporate base a stabilizing force, it can outperform its VC competitors, which are more firmly tied to a ten-year structure for instance. This allows the CVC to make real, long-term investments alongside the corporation’s long-term model approach and design strategic investments that a financial-returns-focused VC cannot do because of its determined life cycle. By imagining longevity from the inception, the CVC can plan for the future without sacrificing to a business unit’s immediate needs or falling prey to market volatility.
2. Create a strong brand and culture: The CVC needs to have its own identity. It must strategically build up its own culture and brand apart from the larger organization. In this way, it should be active in the market and industry under its own brand, even though that brand is somewhat linked to the larger company making use of it’s strengths and core values. Moreover, it should cultivate its own relationships through dedicated partners with key investors. By creating its own network, the CVC can develop its own opportunity pipelines and be understood as the vanguard innovator associated with the corporation.
3. Adopt a core strategy: A CVC must be tied into the corporate strategy and be directly linked to the company’s CEO, research, product development, and marketing. On the other hand, it must be disentangled from the larger corporations business hierarchy. This is the fine line that will define the CVC’s success or failure. The CVC’s partners must have real decision-making authority, and simultaneously have access to corporate resources. By creating a strong, core scaffolding to interact with the larger corporate strategy, the CVC will be able to operate autonomously and take advantage of corporate stability.
Consistency – Transparency – Clear Communication
There’s obviously not one specific way to make a successful CVC. Given distinct corporate cultures, fields, and strengths, there are a myriad of ways to make a successful CVC. However, there are some crucial elements that tend to prove successful. Importantly, the larger corporate structure must allow a CVC to embrace innovation and collaboration. In turn, a CVC should exhibit consistency, transparency, and clear communication about its purpose, goals, and values. By being clear with both investors and the larger corporate structure, a CVC can successfully deploy the strength of a corporate background with the dynamism of a startup. In so doing, a CVC can maximize its ability for long-term market success.