Welcome!

Wearables Authors: William Schmarzo, Shelly Palmer, Liz McMillan, Elizabeth White, Pat Romanski

Related Topics: @CloudExpo, Machine Learning , Artificial Intelligence, @BigDataExpo, @ThingsExpo

@CloudExpo: Blog Post

AI Is About Machine Reasoning | @CloudExpo @ReneBuest #AI #ML #DX #ArtificialIntelligence

What are you going to do when the data only exist in the heads of your employees?

Machine Learning needs tons of data. But what are you going to do when the data only exist in the heads of your employees?

Machine Learning, Deep Learning, Cognitive Computing, Robotic Process Automation (RPA), Natural Language Processing (NLP), Machine Perception, Predictive APIs, Image Recognition, Speech Recognition, Virtual Agent, Intelligent Assistant, Personal Advisor, Chatbot, Semantic Search. Did I miss anything? I am sure I did. However, I guess I provide a good list for your next round of Artificial Intelligence (AI) bullshit bingo. Oh, one last thing - Machine Reasoning! If you've never heard about this term before, just read until the end and you will get its idea and importance for AI.

AI Hits Puberty but Gives Enterprises a New Hope
In 1955 Prof. John McCarthy already defined AI as the goal to develop machines that behave as though they were intelligent. However, according to a Forrester survey after 62 years, the majority of enterprises worldwide are still in an early stage. Around 60 percent researches on AI including market, solutions, platforms, vendors, skills and techniques. Further 39 percent are in the phase of identifying and designing AI capabilities they can deploy and 36 percent are educating the business or building the business case. Only a fifth (19 percent) is testing AI capabilities in their own environment and 14 percent are already training their deployed AI system.

However, enterprises see lot of potential in AI and its technologies as part of a strategic benefit for their organization. Most of them (57 percent) believe that AI will improve the customer experience and support. However, the more interesting part is that 43 percent believe that AI provides them with the ability to disrupt their industry with new business models, products and services. Further 42 percent think, that AI allows them to develop new products and services. I can't agree more on the last two results mentioned, since several customers of ours already have started their AI journey. In doing so, they have started building an AI-enabled Enterprise based on a semantic data graph and the data and knowledge they hold within their entire enterprise stack.

Artificial Intelligence in a Nutshell: About Smart Machines and Teaching Children
Following Prof. McCarthy's AI definition above, we are talking about a vigorous system.

  • A system which must be considered as a raw IQ container
  • A system that needs unstructured input to train its sense
  • A system that needs a semantic understanding of the world to be able to take further actions
  • A system that needs a detailed map of its context to act independently and transfer experience from one context to another
  • A system that is equipped with all the necessities to develop, foster and maintain knowledge

And it is our responsibility to share our knowledge with these machines as we would share it with our children, spouses or colleagues. This is the only way to transform these machines, made of hard- and software, into a status we would describe as "smart", helping them to become more intelligent by learning on a daily basis, building the groundwork to create a self-learning system.

It is kind of rude to compare raising a child with teaching a machine. However, it follows basically the same principles. In 1950, Alan Turing in his paper "Computing Machinery and Intelligence" described the idea of teaching a machine with the essentials of teaching a child. He described three stages:

  1. The initial state of the mind (at birth)
  2. The education to which it has been subjected
  3. Other experience to which it has been subjected that are not to be described as education

Defining these steps of the process, Turing discussed whether it would be more reasonable to program a child's mind and subject the child's mind to a period of education afterwards. He compared a child to a brand-new notebook and thought that it would be much easier to program because of its simplicity.

Get more background on knowledge and the importance for AI in our current Gartner Newsletter "Knowledge is the Ticket to an AI-enabled Enterprise".

Machine Learning in a Nutshell: Jump into Your Data Lake - Again and Again
Machine learning (ML) is a discipline where a program or system can dynamically alter its behavior based on the ever-changing data. Therefore, the system has the ability to learn without being explicitly programmed. In doing so, algorithms enable systems to make data-driven decisions or predictions by building a model from sample inputs. A system then simply does not just memorize the samples but recognizes patterns and regularities within.

The goal of ML algorithms is to find specific patterns in (large) data sets. However, the supreme discipline is to find the right patterns in all related data sources since random patterns can be simply found everywhere. According to Crisp Research analyst Bjoern Boettcher the most common used algorithms right now are:

  • Regression Algorithms
  • Instance-based Algorithms
  • Decision Tree Algorithms
  • Bayesian Algorithms
  • Clustering Algorithms
  • Artificial Neural Network Algorithms
  • Deep Learning
  • Dimensionality Reduction

Once an algorithm has successfully identified a reasonable pattern, further algorithms respectively mathematic procedures can be used to create a new subset of data and identify new patterns. Thus, the entire system is optimizing the existing knowledge or "learning". In general, four types of learning are distinguished:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Semi-supervised Learning

Facebook's News Feed is a good example for machine learning to personalize each member's feed. Meaning, a member who frequently stops scrolling to read or like a certain post of a friend will see more of that friend's activity.

So far, the biggest market of the AI universe seems to be machine learning. At Arago we easily have identified over 100 companies offering solutions and services, including cloud companies like Amazon Web Services, Microsoft Azure or Google. But also smaller companies as well as start-ups are going to try their luck. Ergo, what has started as a blue ocean has quickly turned into a red ocean where the differentiation just turns out in minor parts respectively in the hidden algorithms implemented in the back-ends.

Bottom line, machine learning helps to identify patterns within data sets and thus tries to make predictions based on the existing data. However, most important is to check the plausibility and correctness of the results since you can always find something in endless sets of data. And that's also one of the drawbacks if you consider machine learning as a single concept. Machine learning needs lots of sample data or data in general to learn and be able to find valuable information respectively results in patterns. A fact, Jerry Kaplan highlights as one crucial drawback saying that machine learning is not useful in situations where "[...] there's no data, just some initial conditions, a bunch of constrains, and one shot to get it right."

So, machine learning is basically like jumping into your data lake of endless waters again and again fishing for the next big catch.

Machine Reasoning in a Nutshell: Teaching the Machine with Human Experience

Machine reasoning (MR) systems generate conclusions from available knowledge by using logical techniques like deduction and induction. Thus, machine reasoning systems build the foundation for knowledge-based environments. Reasoning expert Léon Bottou defines [machine] reasoning as an "algebraically manipulating previously acquired knowledge in order to answer a new question". However, reasoning systems come in different approaches that vary in expressive power, in predictive abilities as well as computational requirements. Bottou classifies seven types of approaches:

  • First order logic reasoning
  • Probabilistic reasoning
  • Causal reasoning
  • Newtonian mechanics
  • Spatial reasoning
  • Social reasoning
  • Non-falsifiable reasoning

Everyone who wants to get a scientific perspective on Machine Reasoning I recommend to read the Léon Bottou's paper "From Machine Learning to Machine Reasoning".

Kaplan describes reasoning systems as a concept that deconstructs "[...] tasks requiring expertise into two components: "knowledge base" - a collection of facts, rules and relationships about a specific domain of interest represented in symbolic form - and a general-purpose "inference engine" that described how to manipulate and combine these symbols." As one of the biggest advantages of reasoning systems Kaplan states that based on facts and rules those kinds of systems can be modified more easily since new facts and knowledge are incorporated. In doing so, reasoning systems are taught by "knowledge engineers" who interview practitioners and "[...] incrementally incorporating their expertise into computer programs [...]". This structure makes it also much more convenient to explain the reasoning to the system.

How Does a Sophisticated Machine Reasoning System Look Like Today?

Talking reasoning systems today, the abilities and thus requirements differ from the ones described by Bottou and Kaplan above. Today, an AI technology based on a sophisticated machine reasoning system has the characteristics to empower a system

  • to learn on its own.
  • to find solutions on its own.
  • to discover the world on its own.
  • to understand the world based on concepts (ontology).

The ontology can be explained by how children learn a language. They do learn by listening and then being taught sentences in school together with the right grammar. The ontology is taught by people. People define things for the ontology that should define a common language. And thus, the machine is able to work with that language.

To create a knowledge pool for an AI system, experts need to teach the AI with their contextual knowledge that includes the what, when, where and why. They have to teach the AI with atomic pieces that can be prioritized by the AI. Context and indexing enable these atomic pieces to be combined to form many solutions afterwards.

To achieve the three steps above, a today's sophisticated machine reasoning system is built on four pillars:

  • Learning: First, a system has to be taught. This can be done by single experts or a community is used where people teach the machine bits of knowledge. This is what the machine uses to be able to learn on its own. You might think this way it doesn't learn on its own, but it does. Consider how a child learns. It learns by being taught by his parents, teacher, other children or anyone else teaching things and it just copies and pastes everything with its "sensors" like ears and eyes. Thus, the AI learns best practices and reasoning from experts. Knowledge is taught in atomic pieces of information that represent individual steps of a process.
  • Semantic Graph: The taught knowledge has to be stored, which is done within a data store. The store is used to supply information for the understanding of the world doing semantic reasoning. Like: I know that my mom is connected to dad. And I am connected to my sister. And my sister is connected to her work colleagues. And she works in this city in that building. This is a semantic map of the world that we know. That is part of our memory - a semantic graph. By creating a semantic data map, the AI understands the world in which it operates.
  • Process Engine: The engine is the central back-end service that puts everything together and thus delivers a solution to a certain problem. The engine knows the map of the world where a system is acting in. In doing so, the engine takes everything it knows and finds the correct solution to a specific problem on its own, step by step based on the knowledge it has.
  • Problem Solving: Problem solving also known as machine reasoning (MR) is the ability to dynamically react to change and by doing this, reusing existing knowledge for new and unknown problems. With machine reasoning, problems are solved in ambiguous and changing environments. The AI dynamically reacts to the ever-changing context, selecting the best course of action. Thus, machine reasoning is the basis for a general artificial intelligence (General AI).

Best of Both Worlds: Machine Reasoning Optimized by Machine Learning
So, after all, why is machine learning just a fancy plugin that helps you to get results out of tons of data but also lets you jump into it again and again?

With machine learning you will never be able to adapt to change, which is what every company is looking for. Because change equals innovation! Thus, we consider machine learning as a mathematic optimization technique, which is fully optional. Talking about a decision-making process, everything works correctly without machine learning. Thus, the machine will find a solution on its own. Machine learning can be used to make the way to the solution shorter or more efficient by applying or selecting better knowledge. That's what machine learning is used for.

In our case, machine learning classifies the atomic knowledge pieces in the situation of a certain problem and prioritizes and chooses the better suited pieces to provide the best solution. Thus, machine learning helps to select the best knowledge to a specific state of a problem.

Thus, machine learning as well as deep learning never tells you what, when, where and why a system has solved a problem or has done the decision the way it did. The technologies and algorithms behind are like a black box and you will never get the reason, just a result.

Jerry Kaplan summarizes the pro and cons of machine reasoning vs. machine learning as "[...] symbolic reasoning is more appropriate for problems that require abstract reasoning, while machine learning is better for situations that require sensory perception or extracting patterns from noisy data."

Of course you have to identify which approach fits best for your specific situation. Or in Jerry Kaplan's words "[...] if you have to stare at a problem and think about it, a symbolic reasoning approach is probably more appropriate. If you look at lots of examples or play around with the issues to get a "feel" for It, machine learning is likely to be more effective."

By the way, if you want to read probably the best book on artificial intelligence on the market right now, get Jerry Kaplan's "Artificial Intelligence: What everyone needs to know".

More Stories By Rene Buest

Rene Buest is Director of Market Research & Technology Evangelism at Arago. Prior to that he was Senior Analyst and Cloud Practice Lead at Crisp Research, Principal Analyst at New Age Disruption and member of the worldwide Gigaom Research Analyst Network. At this time he was considered a top cloud computing analyst in Germany and one of the worldwide top analysts in this area. In addition, he was one of the world’s top cloud computing influencers and belongs to the top 100 cloud computing experts on Twitter and Google+. Since the mid-90s he is focused on the strategic use of information technology in businesses and the IT impact on our society as well as disruptive technologies.

Rene Buest is the author of numerous professional technology articles. He regularly writes for well-known IT publications like Computerwoche, CIO Magazin, LANline as well as Silicon.de and is cited in German and international media – including New York Times, Forbes Magazin, Handelsblatt, Frankfurter Allgemeine Zeitung, Wirtschaftswoche, Computerwoche, CIO, Manager Magazin and Harvard Business Manager. Furthermore he is speaker and participant of experts rounds. He is founder of CloudUser.de and writes about cloud computing, IT infrastructure, technologies, management and strategies. He holds a diploma in computer engineering from the Hochschule Bremen (Dipl.-Informatiker (FH)) as well as a M.Sc. in IT-Management and Information Systems from the FHDW Paderborn.

@ThingsExpo Stories
Elon Musk is among the notable industry figures who worries about the power of AI to destroy rather than help society. Mark Zuckerberg, on the other hand, embraces all that is going on. AI is most powerful when deployed across the vast networks being built for Internets of Things in the manufacturing, transportation and logistics, retail, healthcare, government and other sectors. Is AI transforming IoT for the good or the bad? Do we need to worry about its potential destructive power? Or will we...
SYS-CON Events announced today that SIGMA Corporation will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. uLaser flow inspection device from the Japanese top share to Global Standard! Then, make the best use of data to flip to next page. For more information, visit http://www.sigma-k.co.jp/en/.
SYS-CON Events announced today that Daiya Industry will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Daiya Industry specializes in orthotic support systems and assistive devices with pneumatic artificial muscles in order to contribute to an extended healthy life expectancy. For more information, please visit https://www.daiyak...
SYS-CON Events announced today that B2Cloud will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. B2Cloud specializes in IoT devices for preventive and predictive maintenance in any kind of equipment retrieving data like Energy consumption, working time, temperature, humidity, pressure, etc.
SYS-CON Events announced today that Massive Networks, that helps your business operate seamlessly with fast, reliable, and secure internet and network solutions, has been named "Exhibitor" of SYS-CON's 21st International Cloud Expo ®, which will take place on Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. As a premier telecommunications provider, Massive Networks is headquartered out of Louisville, Colorado. With years of experience under their belt, their team of...
SYS-CON Events announced today that NetApp has been named “Bronze Sponsor” of SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. NetApp is the data authority for hybrid cloud. NetApp provides a full range of hybrid cloud data services that simplify management of applications and data across cloud and on-premises environments to accelerate digital transformation. Together with their partners, NetApp em...
SYS-CON Events announced today that Interface Corporation will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Interface Corporation is a company developing, manufacturing and marketing high quality and wide variety of industrial computers and interface modules such as PCIs and PCI express. For more information, visit http://www.i...
SYS-CON Events announced today that Mobile Create USA will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Mobile Create USA Inc. is an MVNO-based business model that uses portable communication devices and cellular-based infrastructure in the development, sales, operation and mobile communications systems incorporating GPS capabi...
SYS-CON Events announced today that Nihon Micron will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Nihon Micron Co., Ltd. strives for technological innovation to establish high-density, high-precision processing technology for providing printed circuit board and metal mount RFID tags used for communication devices. For more inf...
SYS-CON Events announced today that N3N will exhibit at SYS-CON's @ThingsExpo, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. N3N’s solutions increase the effectiveness of operations and control centers, increase the value of IoT investments, and facilitate real-time operational decision making. N3N enables operations teams with a four dimensional digital “big board” that consolidates real-time live video feeds alongside IoT sensor data a...
SYS-CON Events announced today that Suzuki Inc. will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Suzuki Inc. is a semiconductor-related business, including sales of consuming parts, parts repair, and maintenance for semiconductor manufacturing machines, etc. It is also a health care business providing experimental research for...
While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads. In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, will discuss how data centers of the future will be managed, how th...
SYS-CON Events announced today that Ryobi Systems will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Ryobi Systems Co., Ltd., as an information service company, specialized in business support for local governments and medical industry. We are challenging to achive the precision farming with AI. For more information, visit http:...
SYS-CON Events announced today that MIRAI Inc. will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. MIRAI Inc. are IT consultants from the public sector whose mission is to solve social issues by technology and innovation and to create a meaningful future for people.
SYS-CON Events announced today that mruby Forum will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. mruby is the lightweight implementation of the Ruby language. We introduce mruby and the mruby IoT framework that enhances development productivity. For more information, visit http://forum.mruby.org/.
SYS-CON Events announced today that Fusic will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Fusic Co. provides mocks as virtual IoT devices. You can customize mocks, and get any amount of data at any time in your test. For more information, visit https://fusic.co.jp/english/.
In his session at @ThingsExpo, Greg Gorman is the Director, IoT Developer Ecosystem, Watson IoT, will provide a short tutorial on Node-RED, a Node.js-based programming tool for wiring together hardware devices, APIs and online services in new and interesting ways. It provides a browser-based editor that makes it easy to wire together flows using a wide range of nodes in the palette that can be deployed to its runtime in a single-click. There is a large library of contributed nodes that help so...
SYS-CON Events announced today that Enroute Lab will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Enroute Lab is an industrial design, research and development company of unmanned robotic vehicle system. For more information, please visit http://elab.co.jp/.
Agile has finally jumped the technology shark, expanding outside the software world. Enterprises are now increasingly adopting Agile practices across their organizations in order to successfully navigate the disruptive waters that threaten to drown them. In our quest for establishing change as a core competency in our organizations, this business-centric notion of Agile is an essential component of Agile Digital Transformation. In the years since the publication of the Agile Manifesto, the conn...
Real IoT production deployments running at scale are collecting sensor data from hundreds / thousands / millions of devices. The goal is to take business-critical actions on the real-time data and find insights from stored datasets. In his session at @ThingsExpo, John Walicki, Watson IoT Developer Advocate at IBM Cloud, will provide a fast-paced developer journey that follows the IoT sensor data from generation, to edge gateway, to edge analytics, to encryption, to the IBM Bluemix cloud, to Wa...