Blog on Open Artificial Intelligence technology (Opentech AI)

Why? What to expect?

This blog is established as part of a research exchange co-operation between VTT Technical Research Centre of Finland and IBM Research – Almaden. The blog targets to capture the big picture of the research and development on the field of open artificial intelligence technology (Opentech AI), especially focusing on architecture, related ecosystems, progress and future directions. Overview of the topics discussed in this blog around Opentech AI are illustrated in the figure below.


We will use this blog to share hopefully interesting results and findings from the R&D work that we are doing on Opentech AI with colleagues in Finland and in US. As the blog is on Open Technologies AI (code + data + models + stacks + community + leaderboards), we would like to share it basically with whoever is interested.

In addition to blog postings, please note the “Opentech AI Resources” pages for more information on the topics covered. We will update the blog, as well as the resources pages, whenever we come across new interesting things that we think are worth sharing and discussing on the topic of Opentech AI. Comments, discussions and interaction around the postings and topics here are very welcome and greatly appreciated. You are also very welcome to contact us for further discussions and collaboration.

We will try to keep the blog light, interesting and informal. We try to include links for further information enabling you to dig in deeper into the topics of the postings. This is by no means a deep scientific forum – there are specific forums for that (e.g. Arxiv, which is highly relevant for R&D on Opentech AI).

Best regards,

Daniel & Jim


Navigating the Sciences Jungle Around AI

As often stated in multiple sources, AI is a highly interdisciplinary field of research and development leaning on many other sciences (Neuroscience, Linguistics, Anthropology, Philosophy, Psychology, Cognitive Science, Systems Science, Computer Science, Mathematics, Biology, Physics and Social Science). When trying to get grasp of the big picture of AI (like we are) one can really feel this first hand by easily getting lost in a dense jungle of publications coming from variety of different fields, each flavored with different approaches and methodologies of research.

Having experienced this jungle during the past weeks, and finally coming up with some kind of mental map for navigating in it, I thought I could share it in this post. Maybe there are others out there navigating in this same jungle, who could be interested in it as well. It is by no means the only way to see the world around AI, but at least one attempt to have a bit of structure and pathways in the jungle. The map is presented in form of Venn -diagram in the figure below.


The map was created by approaching AI from a viewpoint that in it’s core is system science, computer science and mathematics, which are applied with a common goal of realizing machines mimicking natural intelligence, which interface and interact with other systems in their environment. The map presents numbered interfaces of different sciences overlapping with AI, each representing a pathway between the two sciences. Let’s explore and characterize those pathways a bit:

1. Cognitive science is also an interdisciplinary (Neuroscience, Linguistics, Anthropology, Philosophy, Psychology and AI) field of research studying the cognitive processes of a mind. Cognitive science produces knowledge about the cognitive processes related to intelligence and behavior and has over the past 40 years contributed many cognitive architectures describing and implementing these processes using computing systems of different kinds. This interface is mainly characterized by AI implementing cognitive architectures within intelligent agents, multi-agent systems and other intelligent systems for various applications.

1.1 Apart from Cognitive Science and complete cognitive architectures, Neuroscience has also influenced AI research directly by inspiring AI research to develop and apply different kinds of artificial neurons and artificial neural networks inspired by the structure and function of their biological counterparts in human/mammal brains.

1.2 Apart from Cognitive Science and complete cognitive architectures, Linguistics has also been applied in AI research directly especially in natural language processing, understanding and generation. As a field of research since the 1950’s, natural language processing has used many different approaches from largely hand crafted rather rigid and formal systems to softer statistical probability based systems applying machine learning. In recent years various word vectors (e.g. Word2vec) based deep learning approaches have shown state-of-the art performance in natural language processing tasks and challenges.

2. The interface of AI and biology is twofold. Firstly, intelligence is a natural (biological) phenomena, which AI aims to mimic by creating artificial constructs capable of intelligent behavior observed and judged by their biologically intelligent role models (e.g. humans). Secondly, the AI research and development is bio-inspired as a whole and new discoveries within biology may also trigger new discoveries within AI (e.g potential new findings related to cells, neurons and brain).

3. The interface of AI and physics is also twofold. Firstly, the capability of AI R&D to create artificially intelligent constructs is dependent on availability and applicability of physical materials and energy for building and operating the constructs. Secondly, the physical environment and physical laws are highly relevant for AI applications that operate in the physical word (e.g. AI robotics applications) and may also be relevant to be simulated in those applications that operate in the cyber world only.

4. The interface of AI and Social Science is mainly related to acceptance, impact, need and use of AI applications by human individuals and organisations. Also the value of the AI applications is defined by humans and human organisations in social context.

So what have we learned from our visit to the AI related sciences jungle and why is it relevant to identify the relations between AI and related sciences? Firstly, the progress and success of AI exploitation is highly dependent on ,and influenced by, the progress of R&D on the related fields, which all are still active fields of research with potential for new discoveries relevant for AI R&D.

Secondly, multiple parallel tracks of AI research and development are proceeding in parallel and are not necessarily mutually inter-operable from the viewpoint of building new AI systems and applications (we will continue on this issue later in a separate post). For now, let’s just highlight that the lately mainstream machine learning/deep learning R&D branch of AI has not (at least yet) presented a unified cognitive architecture, as stated in the recent review on cognitive architectures.

Finally, it seems that majority of AI R&D is very task specific and often carried out in isolation (e.g. in game worlds) from the “real world” use environment, problems and social context. The path of transforming a state of the art solution from a game world into an application in a real wold may turn out to be a quite long and complex one to take. Accordingly, we hope to see more data sets, challenges and leader boards with closer link to real world data, application potential, context and constraints.

AI is more than machine/deep learning

How do we approach it?

Artificial Intelligence (AI) was first established as a scientific discipline back in 1956, when it was given the name it still has today at the Dartmouth Workshop. For over 60 years, AI has been researched and developed through multiple hype cycles with up periods of heavy investment and down cycles of lower investment (a.k.a AI winters). There has been, and still are, multiple philosophies and camps that approach AI quite differently. For example, one can categorize the approaches to AI by dividing those according to thinking vs. acting and human like vs. rational.

In our work we do not (at least consciously) position ourselves into a specific school of thought but rather approach AI as open technologies that emulate natural intelligence on a range of tasks exhibited by people, animals, and organizations across a wide range of contexts. Referring to the categorization example, this includes all the categories, as humans are known to be able to both think and act, as well as think rationally and irrationally/creatively.

The mainstream of AI research and development is currently focused in machine learning and its subset of deep learning, which has recently enabled conceptually narrow AI applications to reach or outperform human capabilities in some tasks. Examples of this kind of narrow AI tasks include playing chess (IBM Deep Blue, 1996), Jeopardy! (IBM Watson, 2011) and Go (Google AlphaGo, 2016).

However, on tasks requiring general or conceptually wider intelligence, including learning and reasoning over wide variety of abstract concepts and everyday objects in a given context, the research and development is still in its infancy and not in the level of human intellectual performance. This field of AI, known as Artificial General Intelligence (AGI), focuses in research and development of artificial intelligence that could perform any intellectual task that human can. The AGI R&D is closely related to Cognitive Science; AGI R&D is focused in studying machine realization of human cognition studied by Cognitive Science.

The current state and progress of AI could be summarized by stating that AI is matching human performance in conceptually narrow tasks and the quest is towards a wider AI, perhaps one day matching the general intellectual capability of humans. Interesting ideas, theories, presentations and publications on this subject can be found from e.g. Danko Nikolic, Joscha Bach and Peter Gärdenfors.

Our approach to Opentech AI in our R&D work and in this blog could be characterized also as a quest for wider AI. We acknowledge the power of machine learning and deep learning in narrow AI tasks, but at the same time remain open and curious about all approaches and solutions that have potential for improving the intellectual wideness in AI applications. AI is often contradicted and compared against humans, however the combination of human and machine intelligence has been found to be a very powerful combination and is setting the direction of AI applications towards cognitive intelligent assistants for improving human capability and productivity.