Welcome!

Machine Learning Authors: Elizabeth White, Liz McMillan, Yeshim Deniz, Pat Romanski, Ed Featherston

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.

@CloudExpo Stories
As you move to the cloud, your network should be efficient, secure, and easy to manage. An enterprise adopting a hybrid or public cloud needs systems and tools that provide: Agility: ability to deliver applications and services faster, even in complex hybrid environments Easier manageability: enable reliable connectivity with complete oversight as the data center network evolves Greater efficiency: eliminate wasted effort while reducing errors and optimize asset utilization Security: imple...
High-velocity engineering teams are applying not only continuous delivery processes, but also lessons in experimentation from established leaders like Amazon, Netflix, and Facebook. These companies have made experimentation a foundation for their release processes, allowing them to try out major feature releases and redesigns within smaller groups before making them broadly available. In his session at 21st Cloud Expo, Brian Lucas, Senior Staff Engineer at Optimizely, will discuss how by using...
In this strange new world where more and more power is drawn from business technology, companies are effectively straddling two paths on the road to innovation and transformation into digital enterprises. The first path is the heritage trail – with “legacy” technology forming the background. Here, extant technologies are transformed by core IT teams to provide more API-driven approaches. Legacy systems can restrict companies that are transitioning into digital enterprises. To truly become a lead...
The session is centered around the tracing of systems on cloud using technologies like ebpf. The goal is to talk about what this technology is all about and what purpose it serves. In his session at 21st Cloud Expo, Shashank Jain, Development Architect at SAP, will touch upon concepts of observability in the cloud and also some of the challenges we have. Generally most cloud-based monitoring tools capture details at a very granular level. To troubleshoot problems this might not be good enough.
Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. Until now. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.
SYS-CON Events announced today that CAST Software 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. CAST was founded more than 25 years ago to make the invisible visible. Built around the idea that even the best analytics on the market still leave blind spots for technical teams looking to deliver better software and prevent outages, CAST provides the software intelligence that matter ...
The next XaaS is CICDaaS. Why? Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But… securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous ar...
SYS-CON Events announced today that Daiya Industry will exhibit at the Japanese 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. Ruby Development Inc. builds new services in short period of time and provides a continuous support of those services based on Ruby on Rails. For more information, please visit https://github.com/RubyDevInc.
When it comes to cloud computing, the ability to turn massive amounts of compute cores on and off on demand sounds attractive to IT staff, who need to manage peaks and valleys in user activity. With cloud bursting, the majority of the data can stay on premises while tapping into compute from public cloud providers, reducing risk and minimizing need to move large files. In his session at 18th Cloud Expo, Scott Jeschonek, Director of Product Management at Avere Systems, discussed the IT and busine...
As businesses evolve, they need technology that is simple to help them succeed today and flexible enough to help them build for tomorrow. Chrome is fit for the workplace of the future — providing a secure, consistent user experience across a range of devices that can be used anywhere. In her session at 21st Cloud Expo, Vidya Nagarajan, a Senior Product Manager at Google, will take a look at various options as to how ChromeOS can be leveraged to interact with people on the devices, and formats th...
First generation hyperconverged solutions have taken the data center by storm, rapidly proliferating in pockets everywhere to provide further consolidation of floor space and workloads. These first generation solutions are not without challenges, however. In his session at 21st Cloud Expo, Wes Talbert, a Principal Architect and results-driven enterprise sales leader at NetApp, will discuss how the HCI solution of tomorrow will integrate with the public cloud to deliver a quality hybrid cloud e...
SYS-CON Events announced today that Yuasa System 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. Yuasa System is introducing a multi-purpose endurance testing system for flexible displays, OLED devices, flexible substrates, flat cables, and films in smartphones, wearables, automobiles, and healthcare.
Is advanced scheduling in Kubernetes achievable? Yes, however, how do you properly accommodate every real-life scenario that a Kubernetes user might encounter? How do you leverage advanced scheduling techniques to shape and describe each scenario in easy-to-use rules and configurations? In his session at @DevOpsSummit at 21st Cloud Expo, Oleg Chunikhin, CTO at Kublr, will answer these questions and demonstrate techniques for implementing advanced scheduling. For example, using spot instances ...
DevOps is under attack because developers don’t want to mess with infrastructure. They will happily own their code into production, but want to use platforms instead of raw automation. That’s changing the landscape that we understand as DevOps with both architecture concepts (CloudNative) and process redefinition (SRE). Rob Hirschfeld’s recent work in Kubernetes operations has led to the conclusion that containers and related platforms have changed the way we should be thinking about DevOps and...
SYS-CON Events announced today that Taica 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. Taica manufacturers Alpha-GEL brand silicone components and materials, which maintain outstanding performance over a wide temperature range -40C to +200C. For more information, visit http://www.taica.co.jp/english/.
When it comes to cloud computing, the ability to turn massive amounts of compute cores on and off on demand sounds attractive to IT staff, who need to manage peaks and valleys in user activity. With cloud bursting, the majority of the data can stay on premises while tapping into compute from public cloud providers, reducing risk and minimizing need to move large files. In his session at 18th Cloud Expo, Scott Jeschonek, Director of Product Management at Avere Systems, discussed the IT and busine...
We all know that end users experience the Internet primarily with mobile devices. From an app development perspective, we know that successfully responding to the needs of mobile customers depends on rapid DevOps – failing fast, in short, until the right solution evolves in your customers' relationship to your business. Whether you’re decomposing an SOA monolith, or developing a new application cloud natively, it’s not a question of using microservices – not doing so will be a path to eventual b...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities – ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups. As a result, many firms employ new business models that place enormous impor...
SYS-CON Events announced today that SourceForge has been named “Media 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. SourceForge is the largest, most trusted destination for Open Source Software development, collaboration, discovery and download on the web serving over 32 million viewers, 150 million downloads and over 460,000 active development projects each and every month.
SYS-CON Events announced today that Dasher Technologies 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. Dasher Technologies, Inc. ® is a premier IT solution provider that delivers expert technical resources along with trusted account executives to architect and deliver complete IT solutions and services to help our clients execute their goals, plans and objectives. Since 1999, we'v...