Welcome!

Artificial Intelligence Authors: Liz McMillan, Todd Matters, Yeshim Deniz, Pat Romanski, Elizabeth White

Related Topics: Artificial Intelligence, Machine Learning , @CloudExpo

Artificial Intelligence: Blog Post

Artificial Intelligence Is About Machine Reasoning | @CloudExpo #AI #ML #Cloud

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
No hype cycles or predictions of zillions of things here. IoT is big. You get it. You know your business and have great ideas for a business transformation strategy. What comes next? Time to make it happen. In his session at @ThingsExpo, Jay Mason, Associate Partner at M&S Consulting, presented a step-by-step plan to develop your technology implementation strategy. He discussed the evaluation of communication standards and IoT messaging protocols, data analytics considerations, edge-to-cloud tec...
"When we talk about cloud without compromise what we're talking about is that when people think about 'I need the flexibility of the cloud' - it's the ability to create applications and run them in a cloud environment that's far more flexible,” explained Matthew Finnie, CTO of Interoute, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
When growing capacity and power in the data center, the architectural trade-offs between server scale-up vs. scale-out continue to be debated. Both approaches are valid: scale-out adds multiple, smaller servers running in a distributed computing model, while scale-up adds fewer, more powerful servers that are capable of running larger workloads. It’s worth noting that there are additional, unique advantages that scale-up architectures offer. One big advantage is large memory and compute capacity...
We build IoT infrastructure products - when you have to integrate different devices, different systems and cloud you have to build an application to do that but we eliminate the need to build an application. Our products can integrate any device, any system, any cloud regardless of protocol," explained Peter Jung, Chief Product Officer at Pulzze Systems, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA
With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.
In his session at @ThingsExpo, Eric Lachapelle, CEO of the Professional Evaluation and Certification Board (PECB), provided an overview of various initiatives to certify the security of connected devices and future trends in ensuring public trust of IoT. Eric Lachapelle is the Chief Executive Officer of the Professional Evaluation and Certification Board (PECB), an international certification body. His role is to help companies and individuals to achieve professional, accredited and worldwide re...
The Internet giants are fully embracing AI. All the services they offer to their customers are aimed at drawing a map of the world with the data they get. The AIs from these companies are used to build disruptive approaches that cannot be used by established enterprises, which are threatened by these disruptions. However, most leaders underestimate the effect this will have on their businesses. In his session at 21st Cloud Expo, Rene Buest, Director Market Research & Technology Evangelism at Ara...
SYS-CON Events announced today that Enzu will exhibit at SYS-CON's 21st Int\ernational Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Enzu’s mission is to be the leading provider of enterprise cloud solutions worldwide. Enzu enables online businesses to use its IT infrastructure to their competitive advantage. By offering a suite of proven hosting and management services, Enzu wants companies to focus on the core of their ...
Internet of @ThingsExpo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devic...
Amazon started as an online bookseller 20 years ago. Since then, it has evolved into a technology juggernaut that has disrupted multiple markets and industries and touches many aspects of our lives. It is a relentless technology and business model innovator driving disruption throughout numerous ecosystems. Amazon’s AWS revenues alone are approaching $16B a year making it one of the largest IT companies in the world. With dominant offerings in Cloud, IoT, eCommerce, Big Data, AI, Digital Assista...
SYS-CON Events announced today that Cloud Academy named "Bronze Sponsor" of 21st International Cloud Expo which will take place October 31 - November 2, 2017 at the Santa Clara Convention Center in Santa Clara, CA. Cloud Academy is the industry’s most innovative, vendor-neutral cloud technology training platform. Cloud Academy provides continuous learning solutions for individuals and enterprise teams for Amazon Web Services, Microsoft Azure, Google Cloud Platform, and the most popular cloud com...
SYS-CON Events announced today that MobiDev, a client-oriented software development company, will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. MobiDev is a software company that develops and delivers turn-key mobile apps, websites, web services, and complex software systems for startups and enterprises. Since 2009 it has grown from a small group of passionate engineers and business...
SYS-CON Events announced today that GrapeUp, the leading provider of rapid product development at the speed of business, will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Grape Up is a software company, specialized in cloud native application development and professional services related to Cloud Foundry PaaS. With five expert teams that operate in various sectors of the market acr...
SYS-CON Events announced today that Ayehu will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on October 31 - November 2, 2017 at the Santa Clara Convention Center in Santa Clara California. Ayehu provides IT Process Automation & Orchestration solutions for IT and Security professionals to identify and resolve critical incidents and enable rapid containment, eradication, and recovery from cyber security breaches. Ayehu provides customers greater control over IT infras...
With the introduction of IoT and Smart Living in every aspect of our lives, one question has become relevant: What are the security implications? To answer this, first we have to look and explore the security models of the technologies that IoT is founded upon. In his session at @ThingsExpo, Nevi Kaja, a Research Engineer at Ford Motor Company, discussed some of the security challenges of the IoT infrastructure and related how these aspects impact Smart Living. The material was delivered interac...
Artificial intelligence, machine learning, neural networks. We’re in the midst of a wave of excitement around AI such as hasn’t been seen for a few decades. But those previous periods of inflated expectations led to troughs of disappointment. Will this time be different? Most likely. Applications of AI such as predictive analytics are already decreasing costs and improving reliability of industrial machinery. Furthermore, the funding and research going into AI now comes from a wide range of com...
In his session at Cloud Expo, Alan Winters, an entertainment executive/TV producer turned serial entrepreneur, presented a success story of an entrepreneur who has both suffered through and benefited from offshore development across multiple businesses: The smart choice, or how to select the right offshore development partner Warning signs, or how to minimize chances of making the wrong choice Collaboration, or how to establish the most effective work processes Budget control, or how to ma...
IoT solutions exploit operational data generated by Internet-connected smart “things” for the purpose of gaining operational insight and producing “better outcomes” (for example, create new business models, eliminate unscheduled maintenance, etc.). The explosive proliferation of IoT solutions will result in an exponential growth in the volume of IoT data, precipitating significant Information Governance issues: who owns the IoT data, what are the rights/duties of IoT solutions adopters towards t...
SYS-CON Events announced today that CA Technologies has been named "Platinum Sponsor" of SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. CA Technologies helps customers succeed in a future where every business - from apparel to energy - is being rewritten by software. From planning to development to management to security, CA creates software that fuels transformation for companies in the applic...
SYS-CON Events announced today that IBM has been named “Diamond Sponsor” of SYS-CON's 21st Cloud Expo, which will take place on October 31 through November 2nd 2017 at the Santa Clara Convention Center in Santa Clara, California.