What Went Wrong With IBM’s Watson

The biggest hope for AI in oncology, then, is not that Watson is somehow going to get vastly better. Rather, it’s that Alphabet or someone else has been quietly building a game-changing AI without over-hyping it in advance. It’s still possible that AI might be able to do amazing things in the world of oncology. Just don’t expect those advances to come from Watson.

https://slate.com/business/2018/08/ibms-watson-how-the-ai-project-to-improve-cancer-treatment-went-wrong.html

An ecosystem perspective on Microsoft’s acquisition of LinkedIn

Ecosystem view based on investment styles



Providing a perspective on this ecosystem must be seen in the context of a number of limitations. Business ecosystems are formed based on basic connections between human and organizational relationships. Some of these relationships are physical and others are inferred for example, an organization employs a CEO, CFO, etc and operates primarily at a location and sells a number of products and services to a defined market. Other relationships include sub-organizations, funding and venture related interactions.

Investment styles vary widely, but they all have venture activity and seed funding projects. The ecosystem is tightly integrated with competitive forces creating tight boundaries across services and products offered. If Microsoft wants to get access to the wider Salesforce market, it needed to find the primary data play and enable the core products across their platforms.

Microsoft market performance



Microsoft had a rocky few years under Balmer, but Satya’s focus brought it back on track. He took over the reigns in 2014 and started acquiring key organizations to bolster the core future focus of Microsoft as a key digital player in the cloud.


LinkedIn’s acquisition might not have made sense initially, but the competitive ecosystem showed that primary data is a key competitive force. Google, Apple, etc all focused on owning primary client data and leveraged is across their products. Salesforce already had well developed models as their dominance continued.

Competitive graph approach

The result is an overlapping graph that enables multiple services beyond CRM with primary data. But, not just any data – LinkedIn is the most widely used business network available today. Google’s knowledge graph, Facebook’s social graph and LinkedIn’s economic graph have all created an entirely new way of looking at the world. It’s not that network theory is new, but that these companies have socialized the use of network data across many different constructs. Google has both primary data and links to the world’s knowledge, Facebook has the same and LinkedIn captured a key portion of the incredibly important world.



Looking at all inferences across the ecosystem, it became clear that LinkedIn was a lone player amongst many dominant multi-service strategic technology companies. To further explore and exploit the value created by the vast LinkedIn network it needed to move into a different competitive category. This move will create market pressures where Salesforce and Microsoft ended up fighting over LinkedIn.

Bottom Line

The combination of the new Microsoft strategy and the ability to compete with other big players like Amazon AWS and Google Cloud will propel them into a new competitive space.

The social science of hype

As we accelerate into the world of commercial machine learning and artificial intelligence, we once again find ourselves discussing technology hype. We have to be reminded that each wave of new inventions create benefits​ that are not always quantifiable immediately. If we look back at 1800’s during the Industrial Revolution or we try to understand the early days of commercial flight or the invention and widespread deployment of the motor vehicle, let alone the 80s and 90s when it comes to public access to computing; one sees that the emergence of new platforms are almost inevitable as humans collaborate more and share the world’s intellect. Each wave brings its own set of tools that are closely interlocked with the human psyche of that period.

The social science behind how these new ideas and inventions proliferate is really worth considering. You need to figure out your role and your place in this world of change. There are two schools of thought competing with each other creating social tension that is needed to make these new ideas flourish: One group is facing backwards trying to remember how great the world used to be in some distant past. Then there is another group that is facing into the future, and they try to make sense of all of the current And future trends that could make the more successful. But as we know most people will make decisions without considering many facts and figures and real inferences. Just considering what’s happening in politics today where politicians can make outrageous claims and even contradict scientific findings, and still continue to get the support of certain communities. This strong interplay between the memory of the past and the vision of the future is something all leaders should consider when defining their own journey.

But the world we are moving into might look different. Never before as we enter the face where we’ve got access too so much computational power and human behaviour all data to exploit. This world where the obsessive storage of data about all of the human existence is exposed to any interested party anywhere in the world, is changing our perspective of competition. This ability coupled with almost unlimited access to computational power is changing our understanding of where we fit in the overall landscape. In recent times we’ve seen the re-emergence of artificial intelligence, machine learning, and sub-market or ecosystem classifications, for example fintec, edtech, meditec, etc. This reemergence is field by the access we have to new an improved efficient platforms. Alongside these platforms, Humans have become data collection and consumption machines that can be broadly manipulated by means of intelligent algorithms. The proliferation of these algorithms probably will not take the same shape as in the previous computing generation.

It’s not just about glass anymore, it’s about using all our senses starting with voice and sight. For example this entire post was dictated on my Google Pixel as I see the words appear in my online editor… The Intelligent algorithm figures out the structure of the sentences, the grammar that’s most appropriate and sorting out the punctuation. Furthermore the algorithm has been trying to ignore the sound of Birds and traffic, the background music playing inside my office and the people talking in the distance. It’s solely focused on the words coming from my mouth. This basic capability would not have been possible in recent years.

The one social driver to understand is, random novelty. Novelty is a fairly complex phenomena and I do not wish to talk much about it here, but we have to remember that there are millions of people scattered all over the world that are interested in creating new commercially viable ideas. Novelty is cleverly used by almost all digital and Technology Innovation focused companies to create addiction. It’s the primary purpose of a digital business to create consistent and recurrent client interaction in such a way that maximum benefit can be extracted. We have been educated by the multitude of technologies around us that learned behaviour is the basic entry point. The result is that we become more and more addicted to the incredibly simple and basic responses that are being provided by these various semi intelligent machines around us. The reality is that there are still certain large telecommunications companies, Banks, insurance providers, medical practitioners, financial providers, etc that believe that this trend is not going to have a profound impact on their future. The old days of dumb digital glass will be replaced by intelligent multi-sensory inventions. Believing that creating a mobile app or a well-designed web frontend will make you competitive places you squarely in the rearward facing competitive group.

We have to live with each wave of social Hype as we try to understand the ecosystem’s absorptive capacity for new inventions. Living with this change means that your ability to move with the tight interface between technological invention and social change is probably the only thing that will separate you as a competitive organism in the future.

Living in a networked world: Emergent business ecosystems

When trying to reframe our thinking we need to ensure that there is place for seeing the world for what it really is. I believe that this is one of the most difficult things to do as we’re all stuck in our individual lives and in most cases are not incentivised to think differently. One such example is looking at the way in which the world is organized, where layers and multi-dimensional networks exist all around us. Reductionist thinking gets us to simplify the complexity of these interrelationships to the point where some discretely defined and easily articulated concept is used to describe phenomena.

Networks exist everywhere. But, firstly, a network is described in terms of the elements and relationships among the elements in a system. Systems take on many forms and include; the human body as a system, economic systems, legal systems, etc. Secondly, a network can only exist through two primary reasons, for the sake of this discussion. The first form of existence is the physical manifestation of networked thinking. An example of this type of network is a road system in a country. The road system can connect to another country if access is allowed and the rules of usage are agreed upon. This gets us to the second from of existence, where networks exist in the minds of people. It exists, based on the chaotic and disparate reasoning that exists when humans get connected intellectually.

There are various manifestations of networks, to take the reasoning to the next level. In business there are a number of networks that are closely related physically, but yet well removed in implementation and access. A particular set of networks can include; road systems, telecommunication networks, money networks, social networks, neurological networks and memes. They all have one thing in common, in that each has a node and a relationship to another node. Each node performs a function and cannot exist without at least one other node. The relationship describes the reason for existence and gives the networked system meaning.

Business ecosystems emerge as networks of commerce are formed. In order to develop a detailed view of how competitiveness evolves, different perspectives are required. Here are two examples, a timeline and business network.

The augmented human-technology dilemma

Artificial Intelligence is gaining traction as many parts of the traditional banking process get automated with intelligent algorithms. Machine Learning is often seen as a different area of research in the field of AI, but realistically the entire machine intelligence world is exploding.

machine-learning
On Machine Learning: We might see supervised, unsupervised and re-enforcement learning as the current world, but new algorithms are created that bring big data, artificial intelligence and computational capacity into focus. This drive towards more accurate and predictive machine behaviors, leave us debating the role of humans and the interface with technology. We’re seeing that banking processes are automated and AI assisted by allowing for better ways to on-board customers, more predictive sales approaches, recommendations on transactions, credit score automation, fraud detection, behavior based marketing, etc.

I postulate that we can break this up into three stages namely; basic machine intelligence, response to learning, and augmented machine-human environments. Firstly, machine learning and other parts of artificial intelligence related fields have been implemented in various forms in banking including risk management, detecting fraudulent transactions, etc. All these approaches require the use of large data sets and complex algorithm training environments. Secondly, we’re seeing the focus shifting towards self-correcting and learning approaches where the purpose is to find a combination of algorithms that can behave like humans. This requires access to large, but more reliable, data sets, where the focus is on prediction and automated responses to effectively replace humans in key banking processes. As customers use more digital channels including mobile phones, we find that the need for certain roles disappear entirely in the banking service delivery channel. Thirdly, we are in pursuit of the most profitable interface between human and machine behavior. With a rapidly changing technology landscape comes the new world of redrawing boundaries of where humans are most likely to operate, opening the field of computationally enabled social science. Finding the optimal augmented view of work to be done requires an approach where different classes of algorithms can be used. Infusing these algorithms with human-error, a representation of social behavior, will push us into a new competitive AI ecosystem.

With these inventions moving multiple disciplines into automation, finding your place in this complex ecosystem will be an ongoing challenge.