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Quantum Computers: Bigger Than AI?

Elon Musk, Stephen Hawking and others have been warning about runway artificial intelligence, but there may be a more imminent threat: quantum computing. It could pose a greater burden on businesses than the Y2K computer bug did toward the end of the ’90s.

Quantum computers are straight out of science fiction. Take the “traveling salesman problem,” where a salesperson has to visit a specific set of cities, each only once, and return to the first city by the most efficient route possible. As the number of cities increases, the problem becomes exponentially complex. It would take a laptop computer 1,000 years to compute the most efficient route between 22 cities, for example. A quantum computer could do this within minutes, possibly seconds.

Unlike classic computers, in which information is represented in 0’s and 1’s, quantum computers rely on particles called quantum bits, or qubits. These can hold a value of 0 or 1 or both values at the same time — a superposition denoted as “0+1.”  They solve problems by laying out all of the possibilities simultaneously and measuring the results. It’s equivalent to opening a combination lock by trying every possible number and sequence simultaneously.

Albert Einstein was so skeptical about entanglement, one of the other principles of quantum mechanics, that he called it “spooky action at a distance” and said it was not possible. “God does not play dice with the universe,” he argued. But, as Hawking later wrote, God may have “a few tricks up his sleeve.”

See also: The Race to Quantum Computers  

Crazy as it may seem, IBM, Google, Microsoft and Intel say that they are getting close to making quantum computers work. IBM is already offering early versions of quantum computing as a cloud service to select clients. There is a global race between technology companies, defense contractors, universities and governments to build advanced versions that hold the promise of solving some of the greatest mysteries of the universe — and enable the cracking open of practically every secured database in the world.

Modern-day security systems are protected with a standard encryption algorithm called RSA (named after Ron Rivest, Adi Shamir and Leonard Adleman, the inventors). It works by finding prime factors of very large numbers, a puzzle that needs to be solved. It is easy to reduce a small number such as 15 to its prime factors (3 and 5), but factoring numbers with a few hundred digits is extremely hard and could take days or months using conventional computers. But some quantum computers are working on these calculations, too, according to IEEE Spectrum. Quantum computers could one day effectively provide a skeleton key to confidential communications, bank accounts and password databases.

Imagine the strategic disadvantage nations would have if their rivals were the first to build these. Those possessing the technology would be able to open every nation’s digital locks.

We don’t know how much progress governments have made, but in May 2016 IBM surprised the world with an announcement that it was making available a five-qubit quantum computer on which researchers could run algorithms and experiments. It envisioned that quantum processors of 50 to 100 qubits would be possible in the next decade. The simultaneous computing capacity of a quantum computer increases exponentially with the number of qubits available to it, so a 50-qubit computer would exceed the capability of the top supercomputers in the world, giving it what researchers call “quantum supremacy.”

IBM delivered another surprise 18 months later with an announcement that it was upgrading the publicly available processor to 20 qubits — and it had succeeded in building an operational prototype of a 50-qubit processor, which would give it quantum supremacy. If IBM gets this one working reliably and doubles the number of qubits even once more, the resultant computing speed will increase, giving the company — and any other players with similar capacity — incredible powers.

Yes, a lot of good will come from this, in better weather forecasting, financial analysis, logistical planning, the search for Earth-like planets and drug discovery. But quantum computing could also open up a Pandora’s box for security. I don’t know of any company or government that is prepared for it; all should build defenses, though. They need to upgrade all computer systems that use RSA encryption — just like they upgraded them for the Y2K bug.

Security researcher Anish Mohammed says that there is substantial progress in the development of algorithms that are “quantum safe.” One promising field is matrix multiplication, which takes advantage of the techniques that allow quantum computers to be able to analyze so much information. Another effort involves developing code-based signature schemes, which do not rely on factoring, as the common public key cryptography systems do; instead, code-based signatures rely on extremely difficult problems in coding theory. So the technical solutions are at hand.

See also: Dark Web and Other Scary Cyber Trends  

But the big challenge will be in moving today’s systems to a “post-quantum” world. The Y2K bug took years to remediate and created fear and havoc in the technology sector. For that, though, we knew what the deadline was. Here, there is no telling whether it will take five years or 10, or whether companies will announce a more advanced milestone just 18 months from now. Worse still, the winner may just remain silent and harvest all the information available.

How Is Marine the Heart of Insurtech?

Who would have thought marine insurance would be at the center of the insurtech revolution? The relationship between insurtech and marine insurance is not an obvious one for many people.

Marine is one of the oldest and most traditional classes of business, the origins of Lloyds of London, when from 1686 members of the shipping industry congregated in the coffee house of Edward Lloyd to arrange early forms of marine insurance.

However, two recent announcements firmly place marine in the center of the technology revolution affecting insurance.

First, Maersk announced they are building a blockchain-based marine insurance platform with EY, Guardtime, Microsoft and several insurance partners. Second, a U.K.-based technology company, called Concirrus, announced the launch of the first AI-powered marine insurance analytics platform.

At Eos, this was not surprising.

See also: Insurance Needs a New Vocabulary  

In the first half of 2017, as part of our thesis-driven investment approach, we highlighted commercial insurance as a key area of focus and within that our first product vertical to focus on was marine insurance. What led us to this conclusion?

Commercial marine insurance is a $30 billion premium market, it’s complex and fragmented, and through our analysis we identified a significant potential shift in profit pools over the next few years. Importantly, the emergence of IoT and other devices has created a wealth of data within the industry. Marine also sits at the heart of global supply chain logistics.

During our deep dive into the sector and having spoken with more than 40 market participants across various parts of the value chain, it became apparent that marine insurers (and shippers) have never had so much data (internal and external) available to them, and many don’t have the tools or skill set to take advantage of it.

Growing competition, underwriting capacity and downward pressure on pricing has given little room to maneuver, but we were intrigued and kept digging.

The ability to gather and analyze these new information sources is helpful, but more important will be driving actionable insights through well-informed decision making based on high-quality, real-time data and analytics to improve risk selection, pricing and claim management while helping the insured better manage risk. As with many parts of insurtech, the underlying driver is the move from pure risk transfer to risk mitigation, and from prevention to prediction.

The creation of marine analytics solution platforms provide tailored insights to users, which is an important first step. Currently, software and tech providers to the marine industry are fragmented, with no dominant vendors and no joined up, end-to-end solutions.

As the market matures, the ability to harness analytics capability at the front end with improved efficiency at the back end through blockchain or other initiatives creates an even more compelling story and is an area we will be watching with interest.

Next Step: Merging Big Data and AI

AI is one of hottest trends in tech at the moment, but what happens when it’s merged with another fashionable and extremely promising tech?

Researchers are looking for ways to take big data to the next level by combining it with AI. We’ve just recently realized how powerful big data can be, and, by uniting with AI, big data is swiftly marching toward a level of maturity that promises a bigger, industry-wide disruption.

What to expect from the convergence of big data and AI

The application of artificial intelligence on big data is arguably the most important breakthrough of our time. It redefines how businesses create value with the help of data. The availability of big data has fostered unprecedented breakthroughs in machine learning.

With access to large volumes of datasets, businesses are now able to derive meaningful learning and come up with amazing results. It is no wonder then that businesses are quickly moving from a hypothesis-based research approach to a more focused “data first” strategy.

See also: Setting the Record Straight on Big Data  

But how is big data driving rapid breakthroughs in artificial intelligence?

Businesses can now process massive volumes of data, which was not possible before due to technical limitations. Previously, they had to buy powerful and expensive hardware and software. The widespread availability of data is the most important paradigm shift that has fostered a culture of innovation in the industry.

The availability of massive datasets has corresponded with remarkable breakthroughs in machine learning, mainly due to the emergence of better, more sophisticated AI algorithms.

The best example of these breakthroughs is virtual agents. Virtual agents (more commonly known as chatbots), have gained impressive traction over the course of time. Previously, chatbots had trouble identifying certain phrases or regional accents, dialects or nuances.

In fact, most chatbots get stumped by the simplest of words and expressions, such as mistaking “Queue” for “Q” and so on. With the union of big data and AI, however, we can see new breakthroughs in the way virtual agents can learn by themselves.

IPSoft’s Amelia

A good example of self-learning virtual agents is Amelia, a “cognitive agent” recently developed by IPSoft. Amelia can understand everyday language, learn really fast and even gets smarter with time.

She is deployed at the help desk of Nordic bank SEB along with a number of public sector agencies. The reaction of executive teams to Amelia has been overwhelmingly positive.

Google’s DeepMind

Google is also delving deeper into big data-powered AI learning. DeepMind, Google’s very own artificial intelligence company, has developed an AI that can teach itself to “walk, run, jump and climb without any prior guidance.” The AI was never taught what walking or running is but managed to learn through trial and error.

The implications of these breakthroughs in the realm of artificial intelligence are astounding and could provide the foundation for further innovations in the times to come. However, there are dire repercussions of self-learning algorithms, too, and if you weren’t too busy to notice,you may have observed quite a few in the past.

Microsoft’s Tay

Not long ago, Microsoft introduced its own artificial intelligence chatbot named Tay. The bot was made available to the public for chatting and could learn through human interactions. However, Microsoft pulled the plug on the project only a day after the bot was introduced to Twitter.

Learning at an exponential level mainly through human interactions, Tay transformed from an innocent AI teen girl to an evil, Hitler-loving, incestuous, sex-promoting, “Bush did 9/11”-proclaiming robot in less than 24 hours.

Should the evolution of AI concern us?

Some fans of sci-fi movies like Terminator also voice concerns that, with the access it has to big data, artificial intelligence may become “self-aware” and may initiate massive cyberattacks or even take over the world. More realistically speaking, it may replace human jobs.

Looking at the rate of AI learning, we can understand why a lot of people around the world are concerned with self-learning AI and the access it enjoys to big data. Whatever the case, the prospects are both intriguing and terrifying.

There is no telling how the world will react to the amalgamation of big data and artificial intelligence. However, like everything else, it has its virtue and vices. For example, it is true that self-learning AI will herald a new age where chatbots become more efficient and sophisticated in answering user queries.

See also: Forget Big Data; You Need Fast Data  

Conclusion

Perhaps we will eventually see AI bots on help desks in banks, waiting to greet us. And, through self-learning, the bot will have all the knowledge it could ever need to answer all our queries in a manner unlike any human assistant.

Whatever the applications, we can surely say that combining big data with artificial intelligence will herald an age of new possibilities and astounding new breakthroughs and innovations in technology. Let’s just hope that the virtues of this union will outweigh the vices.

How to Move to the Post-Digital Age?

We are in the midst of the shift from the information age to the digital age, which is realigning fundamental elements of business that require major adjustments to thrive, let alone survive.

As we noted in our new report, Greenfields, Startups and InsurTech: Accelerating Digital Age Business Modelsnew greenfield and startup competitors are rising from within and outside of every industry, including insurance, to capture the post-digital age business opportunities of the next generation of buyers. By shifting to meet the forces of change, these companies are positioning themselves to be the market leaders in the post-digital age. Those that do not make the shift risk not only the loss of customers but also market share and relevance in the coming new age of insurance.

See also: 6 Charts on Startups, Greenfields, Incubators  

Sometimes, the next big thing isn’t easy to spot. The disruption of the insurance industry is in the early days, so predictions are difficult. Will the new greenfields and startups become the next market leaders? If history is a guide, the answer is yes … some will. Just consider Progressive and how many dismissed it early on. Now it is a top 10 insurer in the U.S. Or consider what has happened in other industries with companies that are defunct because they missed the shift:

  • Streaming video: Blockbuster failed to see this trend. It filed for bankruptcy in 2010 and Netflix is now worth more than $61 billion.
  • Mobile games: In 2011, the president of Nintendo North America suggested that mobile game apps were disposable from a consumer perspective. Today, Pokemon Go has 65 million users. Is that disposable?
  • Apple iPhone: Former Microsoft CEO Steve Ballmer reportedly commented that the first Apple iPhone would not appeal to business customers because it did not have a keyboard and would not be a good email machine. Apple iPhone single-handedly disrupted and redefined multiple industries and continues to do so.
  • Autonomous vehicles: In 2015, Jaguar’s head of R&D stated that autonomous vehicles didn’t consider customers’ cargo. Since then, Jaguar Land Rover has invested $25 million in Lyft to join the autonomous trend.
  • On-premise enterprise software vs. cloud-based SaaS platforms: In 2003, Thomas Siebel of Siebel Systems said Microsoft would roll over Salesforce in the CRM market. In 2005, Oracle acquired Siebel Systems for $5.85 billion. Salesforce’s market cap, in contrast, is more than $60 billion.

Insurance Industry Change and Disruption

At no time in the history of insurance can we find as many game-changing events and a rapid pace of advancement occurring at the same time. At the forefront is the increased momentum for insurtech, and the greenfields and startups within, creating high levels of activity, excitement and concern on the promise and potential of insurance disruption and reinvention.

When you add it all up, the insurance industry has many characteristics that make it an attractive target for aggressive investments in innovation. First, its size is enormous – based on industry data, it is estimated that premiums written are more than $4.7 trillion globally. Second, it faces multiple challenges that offer opportunities for exploitation by nimble, efficient and innovative competitors.

Insurtech advancements and the forces of change see no significant slowdown. The momentum for change that has been building is unstoppable. Industry advancements, cultural trends and IT reactions are gaining speed as they gain strength and a framework for stability and growth. It is pushing a sometimes slow-to-adapt industry by challenging the traditional business assumptions, operations, processes and products, highlighting two distinctively different business models: 1) a pre-digital age model of the past 50-plus years based on the business assumptions, products, processes and channels of the Silent and Baby Boomer generations and 2) a post-digital age model focused on the next generation including the Millennials and Gen Z, as well as many in Gen X.

Greenfields and Startups Make the Boardroom Agenda

The market landscape is rapidly changing. During 2016, Lemonade launched. Metromile decided to become a full-stack insurer, leaving its MGA days behind. New MGAs entered the picture, including Slice, TROV, Quilt, Hippo and Figo Pet Insurance, to name a few.  Existing insurers made market debuts with new startups including Shelter’s Say Insurance with auto insurance for millennials, biBerk from Berkshire Hathaway for direct small commercial lines and Sonnet Insurance as the digital brand from Economical Insurance in Canada, among others.

Add to this the projected shrinking of insurable risk pools due to the emergence of autonomous vehicles, connected homes and wearables and the domino effect of these on other industries, and it’s not hard to imagine a future with traditional carriers fighting over a much smaller pool of customers where only the most efficient, effective and innovative will survive.

As a result, discussion surrounding greenfields, startups and insurtech moved into the board room of every insurer and reinsurer trying to understand how to leverage the shift to the digital age and develop strategies and plans to respond. Yet some insurers have a blind spot in recognizing the competition both from outside and within the industry, and the critical need to begin planning a new post-digital age business model. The result is a growing gap between knowing, planning and doing among leaders and fast followers or laggards, which is rapidly becoming insurmountable due to the pace of change.

Closing the Gap with Greenfield and Startup Business Models

Assuming that most insurers grasp the need for a greenfield and startup mentality to grow, what remains is to aim all efforts toward accomplishing an organizational shift. How do you move your company from the pre-digital age to the post-digital age and close the gap?

It requires leadership to build consensus. It requires vision to aim in the most market-ready direction. And it requires a new business paradigm that will allow for change. We must redefine and re-envision insurance to enable growth and remain competitive.

While many have made progress in replacing legacy systems and traditional business processes, this is not enough. These systems, while modern, were built around pre-digital age business assumptions and models, not to support the range of needs in a post-digital age model driven by a new generation of customers. Like other industries, today’s insurance startups and greenfields need and want options that do not require investment in significant infrastructure or upfront costs and therefore seek a cloud business platform solution to maximize options and minimize costs and capital outlay.

See also: How to Plant in the Greenfields  

A modern cloud business platform provides an advantage for greenfields and startups, breaking down traditional boundaries, IT constraints and age-old business assumptions about doing business, while building up the ability to rapidly develop and launch new products and services. The platform is a robust set of technology, mobile, digital, data and core capabilities in the cloud with an ecosystem of innovative partners (many insurtech technology startups) that provides the ability to launch and grow a business rapidly and cost effectively.

Will established insurers suffer at the hands of tech-savvy, culture-savvy competition? Some may, but only if they allow themselves to. There will be constant pressure from greenfields and startups to outdo each other in the race to better meet the needs and demands of a new generation of buyers in a post-digital age for insurance.

For traditional insurance companies, the need to re-invent and transform the business is no longer a matter of if, but of when.  Insurance leaders should ask themselves: Do we have a strategy that considers transformation of both the legacy business and creation of a new business for the future? Who are our future customers and what will they demand? Who are our emerging new competitors? Where are we focusing our resources…on the business or on the infrastructure?

A new generation of insurance buyers with new needs and expectations creates both a challenge and an opportunity that a greenfield and startup business model can capitalize on to incubate, launch and grow. The time for plans, preparation and execution is now — recognizing that the gap is widening and the timeframe to respond is closing.

3 Things on Cyber All Firms Must Know

Managed security services providers, or MSSPs, continue to rise in presence and impact—by giving companies a cost-effective alternative to having to dedicate in-house staff to network defense.

In the thick of this emerging market is Rook Security. I spoke with Tom Gorup, Rook’s director of security operations, about this at RSA 2017. A few takeaways:

Outsourced SOCs. MSSPs essentially function as a contracted Security Operations Center, or SOC. Most giant corporations, especially in the financial and tech sectors, have long maintained full-blown SOCs, manned 24/7/365. And so the top MSSP vendors, which include the likes of AT&T, Dell SecureWorks, Symantec, Trustwave and Verizon, are aggressively marketing MSSP services to midsize companies, those with 1,000 to 10,000 employees.

See also: 7 Key Changes for Insurers’ Cybersecurity  

At the other end of the spectrum—catering to very small businesses—you have consulting technicians, operating in effect as local and regional MSSPs. These service providers may have one or two employees. They make their living by assembling and integrating security products developed by others, working with suppliers such as SolarWinds MSP, which packages and white labels cloud-based security solutions for very small businesses.

So what about the companies in between, those with, say, 50 to 999 employees? Security vendors recognize this to be a vastly underserved market, one that probably has pent-up demand for MSSP services.

What MSSPs provide. For midsize and large enterprises, MSSPs deliver an added layer of expertise that can help bigger organizations actually derive actionable intelligence from multiple security systems already in place, such as firewalls, intrusion detection systems, sandboxing and SIEMs. The top MSSPs tap into all existing systems and provide deeper threat intelligence services, such as device management, breach monitoring, data loss prevention, insider threat detection and incident response.

For small businesses, local MSSPs focus on doing the basics to protect endpoints and servers. This relieves the small business operator from duties such as staying current on anti-virus updates, as well as security patches for Microsoft, Apple, Adobe and Linux operating systems and business applications that are continually probed and exploited.

 Who needs one? Every business today is starkly exposed to network breaches. So who could use an MSSP? The calculation for midsize and large organizations is straightforward. The goal is to provide more data protection at less cost, based on thoughtful, risk-based assessments. The most successful MSSPs will help company decision-makers build a strong case for their services.

See also: Quest for Reliable Cyber Security  

At smaller companies, the first question to ask is this: How mature is my security posture to begin with?

Gorup observes: “Is security even on the radar right now? In smaller organizations, you might have just one person, part-time, working IT. Security is kind of secondary. I’d recommend seeking more advisory services to help detect phishing attacks, help build some processes, help understand what technologies you should invest in. This will allow growth to occur. And then you can make a natural transition into building an SOC or seeking SOC services.”