Archive for the ‘machine learning sdk’ Category

Machines can learn.

Saturday, September 10th, 2011

Check out our newest video (3 min 34 sec). Machines can learn.

 

Machines can learn.

Machines can learn.

 

 

 

 

Artificial Intelligence for Everyone

Wednesday, August 24th, 2011

Artificial Intelligence is the Only Way to Keep Pace

Do yourself (and humanity) a favor — sign up and take Stanford’s class Introduction to Artificial Intelligence (AI). It is free. Open to everyone. And online. You have no excuse. (If the prerequisites of knowing linear algebra and probability theory scare you — then overcome your fear by taking a few of the 10-minute classes offered by Salman Khan at Khan Academy (also free, open and online). I regularly use Sal’s classes to refresh my decades-old memory of many long forgotten math classes. Amazing stuff).

Stanford Professor Sebastian Thrun and Google’s Peter Norvig deserve tremendous credit for making this course available to anyone with an Internet connection. Why?  Because if you don’t understand artificial intelligence you won’t understand the future. Stanford and AAAI are showing the kind of leadership in education that  that can (and probably will) spawn a new wave of innovation that will transform our lives even more than the Internet.

This class is so important that everyone at ai-one signed up — even though we already know a bit about artificial intelligence ourselves (which is often called machine learning). My fiance, father, cousins…even my workout buddy also signed up. If nothing else, this class is taught by two of the smartest people working on how to solve problems faster and more accurately by using machines that can learn how to reason and learn patterns with ever decreasing human intervention.

Machine Learning is Very Different from Machine Programming

I often speak with prospective customer for our technology who immediately ask me what a learning machine can do differently than a computer that is programmed. The answer is simple but profound: Machines can now learn like humans — by detecting the meaning of data by detecting inherent patterns and associations of each element within a data set. This means the machine can learn the meaning of whatever data you feed to it. No, it can’t reason — that is, machine’s can’t spontaneously create new thoughts (yet). They can spontaneously detect how a word is related to other words, documents, websites, etc. This way of determining meaning through association is often called a semantic network. Although the concepts for creating a world wide web of semantically linked data has been around for a long time (notably described by Tim Berners-Lee in his famous paper The Semantic Web in 2001).

Linking data is the only to make sense out of it. Without links it is simply a sea of noise. Noise that is growing at an astonishing rate.

Evolve or Die: Why Everyone Needs to Know About Artificial Intelligence

Human currently doubles every 5 years — your cognitive capacity does not.

In fact, cognitive capacities are much the same for any individual human as they were before we learned enough to form civilizations. So we are only as smart as our capacity to learn — and that capacity has limits. One such limit is the Dunbar Number which is the theoretical limit of the number of people with whom you can maintain meaningful relationships. This is thought to be between 100 and 200 people. So even though I might have over 800 Facebook friends — most are people whom I do not have sustainable, long-term relationships. Many of these “friends” are people I knew in high school and have long since lost contact (except through Facebook). Interestingly, about 142 people made a personal effort to wish me a happy birthday (130 were on Facebook) — reflecting a value  that falls within widely accepted values for the Dunbar Number (which can be thought of as a Dunbar Limit).

The news for your brain gets worse. Knowledge is continuing to grow faster. Several leading indicators  (such as the adoption newly patented technologies) indicate that this pace will increase exponentially — as predicted by Ray Kurzweil in his now famous essay The Law of Accelerating Returns. Data grows even faster than human knowledge. Data includes both the factual information (that is useful) and all the outputs of sensing devices. Knowledge is the extraction of meaning from data.

Cisco’s Dave Evan’s estimates there are about 35 billion sensors connected to the Internet — enabling an internet of things. That works out to 7 devices for every human on the planet — and growing.

Artificial intelligence is the only way for humans to evolve as fast as our data. If only a few people know about artificial intelligence then only a few people will reap the benefits. Knowing about AI is essential for us to ensure a future filled with greater liberties and opportunities for everyone’s mutual benefit.

Example to Illustrate Difference Between Data and Knowledge

Sensors record data. To make that data useful (actionable or meaningful) we must use systems (such as software) to process the data into information. For example, my heart rate monitor records each heartbeat and my location over time (it is GPS enabled). I know the exact time and place for each contraction of my heart as indicated by an electrical signal. Each of these data points is meaningless unless I can see a pattern of how all those heartbeats fit together. My goal is to see a pattern where I run faster at a lower heart rate. My monitor is old — so it takes me about 20 minutes to download the data, look at it (using the really bad software that came with the system), then determine if how I am progressing (or not). NONE of this data links anywhere. So it is useless to my  insurance company — too bad because I’d like them to know that I am fitter than the average person so they can lower my health insurance rates.

Big Data

The explosion of data caused by all the billions of people and billions of sensors offers a tremendous opportunity to find new value — both in terms of new ways to make money and new ways to make discoveries to improve the human condition.

It is comical when business leaders complain about “big data” problems — rather than seeing big data as a massive, unprecedented opportunity to gain competitive advantage by understanding more than competitors. IDC’s 2011 Digital Universe Study provides great insights on how businesses can “extract value from chaos.”

Big data is a relative term. Thirty years ago, it was unimaginable to have a way to access a terabyte of data. Now I can access 10,000x more than that — from my cell phone. Thirty years from now, my great-nieces and nephews will scoff at our struggles to make sense of exabytes of “chaotic” data (absurd because chaos is only a matter of not seeing inherent patterns within data). The story of science is the never ending discovery of new patterns in things we considered random, chaotic (or divine), such as: weather, astronomical events, plagues, diseases, etc.

Making Sense of It All

We recently released an application program interface (API) that enables programmers to build artificial intelligence into software applications. The value of this API is that is generates a lightweight ontology that reveals all patterns and associations within a data set. Feed it data. It tells you how any one element (byte, word, document, etc.) relates to another. Here is a link to a video that describes ai-one’s machine learning technology.

Yes, you can get a no-obligation copy to try for yourself — just contact us.

Overview Video of ai-one Topic-Mapper SDK for Machine Learning Applications

Monday, August 1st, 2011

Click this link for an video overview of the ai-one Topic-Mapper SDK for machine learning applications (8 minutes).

Leader of Knowledge Management and Text Analytics Joins ai-one Partner Network

Tuesday, July 12th, 2011

Press Release

KAPS Group plans to use new machine learning SDK to build advanced knowledge management applications for enterprise clients.

La Jolla CA | Oakland CA – KAPS Group announced today they would start using ai-one’s machine learning technologies to build custom applications for large corporations and government agencies. KAPS specializes in designing and developing systems that add semantic intelligence to unstructured content. These systems range from enterprise search to sentiment analysis-based customer intelligence to knowledge management systems that enable organizations to capture and use the information that employees learn through years of on-the-job experience. These systems are becoming increasingly important as companies struggle to retain expertise as expert employees retire or leave the company.

Semantic Structure Key to Knowledge Management

Tom Reamy, Chief Knowledge Architect at KAPS, sees the market continuing to grow. “There is a growing realization that adding semantic structure is the only way to make sense of all the extremely valuable, unrealized content that resides in today’s organizations. This is true for the information and knowledge in documents and in the expertise of employees. And capturing, organizing, and structuring that information is what will drive companies to be more innovative, responsive, and profitable.”

The Value of Machine Learning

KAPS partnered with ai-one inc to gain access to software development kits (SDK) that enable programmers to build machine learning into other applications. These SDKs make it possible for computers to automatically read and learn the meaning of vast amounts of unstructured data by how words are associated with each other. For example, a system might read millions of emails on a drug discovery process to learn that two chemicals could achieve the same result – even though one of the chemicals was ignored by a research team.

“It is an honor to have KAPS as a partner,” said Olin Hyde, ai-one’s VP of Business Development, “they have a fantastic reputation for building state-of-the-art systems.” Previous KAPS clients include the FDA, GAO, Genentech, Visa and Amdocs.

About KAPS Group LLC

Led by Chief Knowledge Architect, Tom Reamy, KAPS is a group of knowledge architecture consultants with a wide range of skills and experience. The firm’s services include: text analytics categorization and entity extraction catalogs, taxonomy creation, design and implementation of metadata and controlled vocabularies, implementation of search, content management, and portals, and strategic consulting. Based in Oakland California, KAPS Group’s services are grounded in the creation and maintenance of the intellectual infrastructure of an organization. This intellectual infrastructure consists of a wide variety of content and knowledge structures from metadata and taxonomies to linked data and ontologies, information technologies, the information processes embedded within business procedures, and the information/knowledge needs and behaviors of individual people and social communities.

Contact: Tom Reamy Phone 1-510-530-8270, email: tomr (at) kapsgroup (dot) com web: www.kapsgroup.com

How to Use ai-one’s Machine Learning SDK: Insights from an expert programmer

Friday, July 8th, 2011

Ondrej Florian is one of ai-one’s leading experts in developing machine learning applications. Ondrej joined the ai-one Consulting Partner program in February 2011 and is currently building systems for financial services clients from his office in Basel, Switzerland. (Since this interview was posted, Ondrej has joined the ai-one team full-time).

Olin Hyde, ai-one’s VP of Business Development, recently interviewed Ondrej Florian who answered questions about his experience using ai-one’s Topic-Mapper SDK for machine learning.

Interview with Ondrej Florian GER Version- Für eine deutsche Version dieses Interviews finden Sie hier

What has been your experience using ai-one’s technology?

It has been a long journey. I was one of the first developers to use Topic-Mapper. At first I confused by what it actually does, more then anything. Now I love it. It is amazing because it opens so many possibilities to build really cool, smart applications.

What changed your mind about ai-one? Isn’t it hard to like a system after you have a rough start?

First I had to understand that artificial intelligence (AI) is not a magic bullet. Instead, it is a completely different way to look at programming.

They key is:  You need to find the core problem then see how to use AI to build a solution.

When I first started using it, I was frustrated with the results that Topic-Mapper gave me. It felt like the answers were wrong. The system seemed to have a life of its own. I fed it data and it gave me very perplexing answers. I thought, “That cannot be right.”

Then I realized that the system was only learning what it was exposed to.

If you feed it a little, then it only knows a little. The more you feed it, the more it knows.

I love it now. It makes me think in a different way. The possibilities for developing AI applications are only limited by my imagination.

What is so different about programming with ai-one technology?

It is more like a conversation than programming. You can think of AI as giving the computer an empty brain. With AI, it has the capacity to learn and act independently.

As a programmer, I am used to computers doing what I tell them to do. They only follow directions. With AI they start to find relationships independently.

With ai-one, I had to start thinking in a different way. You must think like teaching the computer – not programming it.

Inspiration is really important. The technology allows you to do so much more than what I understood at the beginning. It is not a problem with the technical documentation as such.  The API is very simple. Very straightforward.

The hardest part is to really understand two things:

  1. What are the core problems you want to solve with AI?
  2. What data is necessary for the system to solve the problem?

Forget your ‘assumptions’ you may have about how the system should work, don’t force it.

Once you have these two questions answered, then you can think about lower level questions, like: How can I make it easier for the system to read the data the same way I do?

The key to getting the most from the technology is to think about solving problems in a totally different way. I was told this at the start by Tomi Diggelmann (ai-one’s VP of Technology). He said: “It is important to get programmers to think differently or they will not understand what they can do with ai-one.”

How they should they think about problems? What is so different about it?

ai-one is very different than the traditional API – which is just a functional technology stack (e.g., LAMP). Traditional systems have a simple goal — to extract results. Programmers look for algorithms to solve a problem, using code to sort, match, extract data.  All you need to do is read the documentation, develop test cases, then build the application to meet the test cases. The hardest part is often making sure the data is in the right format, complies with your structure, and so on.

ai-one is different. It is very dynamic. First, you don’t have to worry about data formats. You just feed data into the holosemantic space. This is may be possibly the import command. It will accept any kind of data. It makes associations among all the elements as it ingests the data.

You curate the data by asking the system questions. It will reveal a lot of associations. You teach the system to know the data the way you know it by providing it with commands for context, associations and keywords.

Are there a lot of commands to know?

Really there are just four general functions you must understand: association, reverse_association, association_check and keyword. You use these commands to ask the system to tell you what it has learned from ingesting the data. It will give you results back. And they are sometimes confusing. At beginning you get nothing or things that are seemingly irrelevant. This is a sign that you have given the system too little information. If you give the system enough information it will give you easily over 80% correct answers the first time, without any teaching. The more data, the more accurate it becomes.

ai-one” listens” to the data then tells you what the data means. This eliminates the editorial bias of the programmer.

Yes, you can teach ai-one to give you the right answer – but this must be done carefully. Like an obedient child, the system will learn exactly what you tell it.

So you just feed the system information and it gives you meaning? That sounds too simple.

You must structure the data in the right way. Teaching is only to correct mistakes. Associations that should not be there.

I understand you were frustrated when you first used the SDK?

Yes. It was confusing to me because I had to teach rather than program. Under normal circumstances the machine will only do what it is programmed to do. In a way, ai-one’s SDK has a life of its own. It learns associations based only on the inherent semantic value of the data.

Programmers must learn why the data gives them the results. Programming with ai-one’s Topic-Mapper is almost like a conversation between the programmer and the system.

So how can programmers get up to speed quickly?

Working with ai-one is interactive. The machine will tell you what it sees – the programmer must be able to set aside assumptions and see how the machine is learning. The advantage of ai-one is that it will tell you how it is forming associations.

When you interact with the system – you must think past “bugs.” The results are not bugs – they are what the machine is seeing!  It sees the data from an inherent meaning – must be structured in way that the machine sees in the way you want it to see. Remember, the machine has no bias.

You must observe and learn from the results you are getting back.

People are still trying to determine how to use AI. It has been around for a long time. And it has failed many times. What makes this different?

From a programmer’s perspective, it comes down to inspiration. Rather than programming, you are influencing. Teaching the machine to augment human understanding.  ai-one’s SDK enables the programmer to find unknowns at the start – rather than when the program breaks from having an inadequate algorithm.

Do you have a case example where ai-one’s SDK has solved an unknown problem?

Many. For example, in risk management there are many ways for a person to cheat and steal from a financial institution. So how do you monitor and prevent it?

Traditionally, programmers would use SQL to run queries against a database and use rules to isolate variance then model the variance using algorithms. Risk evaluation is essentially static – you only program what you can know. This is doomed to fail. You can’t possibly know all the risk factors.

ai-one allows you to find the unknown – the unexpected relationships between data elements that are associated with risk. You let the system tell you what data elements are associated with risk then model those!

A lot of people don’t believe ai-one’s claims. They consider them too good to be true. How do you address this doubt and mistrust?

You can’t address it by arguments. Programmers like control. This is the problem. To control something, you must know a lot about it. When data gets really big and complex, it becomes impossible to know it enough to control it.

What changed my mind about ai-one is when I understood that the SDK enables me to understand big data so that I can use machine learning to augment systems to model data more accurately.

Statistical approaches are great at handling what is known.

ai-one’s technology is not a replacement for algorithms or programming – rather it is a way to enhance the value of all the great things programmers can do!

The way to address this skepticism is with demonstration, to take them through the same experience and let them discover it for themselves.  That’s why I signed up to help ai-one with the training for new programmers.

Crystal Reports Guru Embraces Machine Learning

Wednesday, July 6th, 2011

Press Release

DotNet Tech plans to use new machine learning SDK to bring advanced analysis of unstructured data into Crystal Reports services.

La JollaCA| Zurich| Berlin– Crystal Reports is about to get a lot smarter. Brian Bischof, widely regarded as the leading authority on Crystal Reports, just signed a deal to become an IT Services Consulting Partner with ai-one inc. The deal will give Bischof’s company, DotNet Tech, access to ai-one’s Topic-Mapper SDK to develop custom reporting tools that use artificial intelligence to report on unstructured data.

Crystal Reports, owned by SAP, is one of the most popular tools to create reports using information stored in databases. One of the biggest problems facing IT departments is reporting on data that is not easily categorized – such as feeds of text from the internet and social media.

ai-one’s Topic-Mapper enables Crystal Reports to read and ingest text in much the same was a human. This is the first deal that enables a consulting firm to build artificial intelligence into a common reporting tool.

“Gone are the days of trying to work around unstructured data,” said Bischof, “Now we can use ai-one’s Topic-Mapper to learn the inherent structure within any corpus.  Now we can process and include everything from Twitter feeds and Facebook postings into Crystal Reports using Microsoft’s Visual Studio.

Olin Hyde, VP of Business Development added “I’ve known Brian for a long time. He is a fantastic, visionary developer. I can’t wait to see what he does with Topic-Mapper combined with Crystal Reports and Visual Studio.”

About DotNet Tech, Inc., Led by founder, Brian Bischof, CPA is a systems consulting firm specializing in the development of advanced web-based applications using Microsoft’s .NET suite of development tools. Bischof is also the best-selling author of Crystal Reports books. Clients include University of California San Diego and more than 10,000 subscribers to www.CrystalReportsBook.com.

Contact:  Brian Bischof.  Phone 1-502-417-3681, email: public@bischofsystems.com web: www.dotnettech.com

About ai-one inc., ai-one provides technologies that enable programmers to build artificial intelligence into software programs. Based inSan Diego with offices inZurich andBerlin, ai-one’s “biologically inspired intelligence” is a virtual brain that learns without human intervention. Technically described as an adaptive holosemantic data space with semiotic capabilities, ai-one’s approach provides more accurate answers than competing technologies.  The Topic-Mapper™ SDK for text enables developers to create intelligent applications that deliver better sense-making capabilities for semantic discovery, lightweight ontologies, knowledge collaboration, sentiment analysis, artificial intelligence and data mining.

 

Contact: Olin Hyde, Phone: 1-858-381-5897, email: oh@ai-one.com, web: www.ai-one.com

 

AI Goes to Wall Street: Trading Platforms Get Smarter

Thursday, June 30th, 2011

Press Release

It is no secret that for many years global banks have used artificial intelligence to make better trades. Now that technology might be coming to your local independent investment advisory service.

Caapi Technologies just announced that it signed a deal to use artificial intelligence technology to build custom trading systems for small to mid-size investment firms. Caapi will build applications with software development kits (SDKs) from ai-one that enable computers to understand human language to find undervalued stocks, bonds and derivatives.

The partnership makes Caapi one of the first consulting firms to use ai-one’s machine learning technology to build trading algorithms and platforms for traders, banks and hedge funds.

Building custom trading algorithms is a huge industry propelled by the success of high-frequency trading across global markets. Originally, these algorithms were designed to find and exploit pricing differences between stocks, commodities and derivatives. Now trading algorithms are so widespread and so sophisticated that they have completely reshaped markets to the point where pricing is often driven more by speculation than it is by the underlying value of the asset class.

The challenge now is to find underpriced opportunities that generate returns based on actual performance rather than market volatility. This requires that investors sort through vast amounts of unstructured data to find undervalued assets before they are identified by the rest of the market. Often this means reading text that can’t be processed by search engines like Google. Traditional algorithmic approaches, such as Google’s, fail as they only know what they are programmed to know or programmed to find. They miss finding unexpected results that don’t fit into an equation.

ai-one’s technology is described as “biologically inspired intelligence.” It is modeled after the human brain and does not depend on algorithms. Rather, it automatically sees the inherent patterns within data and forms associations between each data element. This enables machines to learn without any human intervention. More importantly, it enables people to ask the questions generic medication zoloft they wouldn’t normally know to ask.

The CEO of Caapi, Mr. Moris Oz, sees machine learning as the key to discovering hidden investment opportunities. “a-one’s technology enables us to build semantic associative search engines for our clients that understand how the price of any given investment is related to the unstructured data found on the internet.”

Caapi’s approach is to combine proven techniques using sophisticated algorithms with machine learning that understands words.  “Language is not math,” adds Olin Hyde, VP of Business Development at ai-one. “Algorithms are fantastic at processing structured data. But human behaviors and communications are inherently unstructured and complex. We learn through words not equations. So why not enable computers to do the same?”

According to Moris Oz, CEO of Caapi, “ai-one’s SDK for machine learning could be the answer for understanding and correlating soft data driving price moves in the markets.  I’m looking forward to applying this to new applications.” The market will soon tell if it works or not.

About Caapi Technologies, Founded by Moris Oz, the company offers consulting, system engineering, Algo trading machines and rigid body physics simulations. They design, program and deliver complex algorithmic and automated trading platforms. Caapi’s expertise spans the most common technology platforms such as Java, .NET, GWT, Flex, PHP, CSS, JS, Facebook SDK etc., for building scalable, feature-rich Web applications. Based in Israel, Caapi services encompass project management, software design, software development, quality assurance, documentation, and technical support.

For more information see http://www.caapitech.com

Contact Moris Oz, Ph +972-9-8656875 email moris@caapitech.com

About ai-one inc., ai-one provides an “API for building learning machines”.  Based in San Diego, Zurich and Berlin, ai-one’s software technology is an adaptive holosemantic data space with semiotic capabilities (“biologically inspired intelligence”).  The Topic-Mapper™ SDK for text enables developers to create intelligent applications that deliver better sense-making capabilities for semantic discovery, lightweight ontologies, knowledge collaboration, sentiment analysis, artificial intelligence and data mining.

For more information see http://www.ai-one.com

Contact: Olin Hyde, Ph: 1-858-531-0674, email: oh@ai-one.com, web: www.ai-one.com

 

The Singularity Just Got A Lot Closer

Thursday, June 2nd, 2011

New tool allows programmers to build artificial intelligence into almost any software application.

SDK for Machine Learning

A new technology enables almost any application to learn like a human. The Topic-Mapper software development kit (SDK) by ai-one inc. reads and understands unstructured data without any human intervention. It allows developers to build artificial intelligence into almost any software program. This is a major step towards what Ray Kurzweil calls the technological singularity – where superhuman intelligence will transform history.

Unlike other machine learning approaches, ai-one’s technology extracts the inherent meaning of data without the need for any external references. A team of researchers spent more than eight years and $6.5 million building what they call “biologically inspired intelligence“ that works like a brain. It learns patterns by reading data at the bit-level. “It has no preconceived notions about anything,” explains founder Walt Diggelmann, “so it works in any language and with any data set. It simply learns what you feed it. The more it reads, the more it learns, the better it gets at recognizing patterns and answering questions.”

Technical Advances

Lightweight Ontologies (LWO)

The technology incorporates two major technical advances: First, it automatically creates what ai-one describes as a “lightweight ontology” (LWO), The system determines the relationships between data elements as they are fed into the system. The primary benefit of LWO is that it is completely objective — it makes associations without editorial (human) bias. LWOs are also very adaptive, automatically recalculating when ingesting new data. Unlike traditional ontologies, LWOs require no maintenance.

Dynamic Topologies

Second, ai-one’s technology generates “dynamic topologies” that transforms the data structure to find the best answer to any question. The benefits of dynamic topologies include incremental learning – the system buy sertraline hcl online gets smarter as it is exposed to more questions. Moreover, it is able to deal with ambiguity and unknown situations. The result is that the system can answer the questions that a person wouldn’t normally know to ask.

The SDK opens the door for many new, disruptive software applications. For example, it can replace search algorithms with more accurate “answer engines” that deliver the most precise answer to any question.

“We offer a core programming technology,” says Tom Marsh, President and COO of ai-one. “The possibilities are almost endless. Our business model is to license the SDK to software developers to build end-user applications. Our goal is to get Topic-Mapper to as many well-qualified programmers as possible and let the creativity of the market take over.”

Adoption has been quick.

The first version of the SDK was released in February 2011. In less than three months, more than 20 consulting partners signed up to use the technology to build commercial applications – mostly in Europe. Swissport matches passenger manifests against the US Department of Homeland Security’s No-Fly List. The core technology is used by Swiss law enforcement CSI labs to match shoeprints and other evidence from multiple crime scenes. Most recently, ai-ibiomics announced it will use ai-one’s SDK to read genome sequences to provide personalized medical services in Germany.

A logical next step is for the technology is to enable eCommerce, social media and other online applications to provide end-users with the most relevant, most accurate information for any given situation.

ai-one will be showcasing the technology at booth #107 during the SemTech 2011 conference on June 7-8. Developers can request a 30-day evaluation copy online at http://www.ai-one.com/solutions/semtech2011.