Anthronoetic Artificial Intelligence

Artificial intelligence has made great strides, integrating algorithms with big data to infer answers for critical strategic, societal, and scientific problems. NoME’s Anthronoetic (human-like) Artificial Intelligence is the next generation of sophistication: an artificial intelligence designed to think in a human style.

Surpassing Neural Nets

Oceanit is developing the next-generation AI: Noetic Mathematical Engine (NoME).Through this approach, we will replicate the human thought process in computers and surpass the current, big data approaches of neural networks.

Natural Language

NoME focuses on natural language processing, a linguistic-centric approach designed to think in a human style. This means an AI that understands fluidity; the nuance and recursive nature of human cognition.

Explanatory AI

NoME's Anthronoetic style of artificial intelligence offers explanations to accompany answers. This enables a user to understand both what the NoME output or answer was and understand how NoME arrived at that output.

Noetic Mathematical Engine

NoME is a unique AI utilizing linguistics and analogy algorithms to derive not only answers and descriptions, but meaning and explanations.

An AI that can be understood

One of the most popular AI frameworks in recent years is deep neural networks, whose structure is based on a simplified model of the network of neurons in the brain. Indeed, Oceanit has used deep networks in many applications and demonstrated state of the art levels of performance. However, understanding the human brain is difficult, and similarly understanding why a deep network makes the predictions it does can be difficult or even impossible. Our goal with NoME is to create an AI system that overcomes this limitation, and whose decisions can be easily understood by and explained to the user.

Linguistic-based architecture

Rather than drawing inspiration from how the brain functions physically, our approach is anthronoetic - that is, it is based on how humans reason abstractly. Indeed, humans do not reason at the level of a neuron. Instead, we create complex combinations of a richer set of concepts. To describe what these concepts are and how they can interact, NoME uses ideas from Chomskyan linguistics. This provides both a theory for the structure of human cognition and the means for conveying those thoughts to a user.

Explanations not predictions

The difference between anthronoetic and statistical AI is best seen in their outputs. Statistical approaches such as deep networks simply generate predictions (only answering “What?” questions). By contrast, our anthronoetic approach creates explanations (answering fundamental “Why?” questions), from which predictions follow. This additional explanatory level of output enables NoME to demonstrate how it derived its conclusion, and thus work in conjunction with experts, who need to trust their AI collaborator, particularly in safety critical applications where the AI requires a human in the loop to correct any costly mistakes. Importantly, such error-correction works both ways: humans checking AI and AI checking humans so as to eliminate error, converge on truth, and make unlimited progress.

Our long term goal is to build artificial general intelligence. We are passionate about changing the world with science and software, and we are looking for exceptional people to join us in that mission.

NoME is a high impact team that’s building the next generation of intelligence and language understanding. To achieve this, we’re working on projects that utilize the latest techniques in artificial intelligence. We strive to be the best and the brightest in the fields of linguistics, AI, cognitive science, computer science, mathematics, biology, genomics, and beyond.  We are looking for software engineers and other talented people that have applied experience to join our team.

As a software engineer in NoME, you work on a small team, collaborating closely with other product teams and you’re able to switch projects as our fast-paced business grows and evolves. We need our engineers to be versatile and passionate to tackle new problems. Because you’ll be working with cutting-edge technology, you’ll also get a chance to work closely with researchers across our parent company, Oceanit.

Minimum qualifications:
– BA/BS in Computer Science, related technical field or equivalent practical experience.

Preferred qualifications:
– MS or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
– Experience with one or more general purpose programming languages including but not limited to: Java, C/C++, C#, Objective C, Python, JavaScript, or Go.

Types of Artificial Intelligence

The widely accepted types of artificial intelligence are Narrow AI, General AI, and Superintelligence

NoME Development Trajectory

Anthronoetic AI will grow at an accelerated rate

Infant - Years 1-2

Toddler - Year 3

Adolescent - Year 4

Adult - Year 5

The Next Level of Sophistication

NoME is strong artificial intelligence: a computational system designed to “think” the way a human thinks. NoME processes and understands natural language by running computations that are formally isomorphic to those running on human

The 'weak AI' of Google, Apple, and IBM is typified by unintelligent, brute force methods that can only retrieve general information given explicitly in the data. By contrast, NoME generates specific information - and explanations - by understanding user queries, searching the data intelligently, discovering (possibly implicit) semantic content, and communicating its answers.

Frequently Asked Questions

Here are some FAQs

1. What are the definitions of Artificial Intelligence and Machine Learning?

Artificial intelligence is the capability of a machine to emulate intelligent human behavior. Machine Learning (ML) is the science and engineering of endowing computers with the ability to learn without explicit preprogramming.

AI and ML are often conflated with one another, but it is important to understand that not all AI techniques use Machine Learning and that ML is not genuine AI.

2. What are neural networks?

Neural networks are a subset of machine learning algorithms.  They are computer systems based on super-simple models of the human brain.

The ‘neuron’ is the computational component and the ‘network’ is how the neurons are connected. Neural networks pass data among themselves, accumulating patterns as the data flows.

3. What is deep learning?

Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways with task-specific algorithms.

This enables the machine to ‘train itself’ to accomplish certain tasks like speech- or image-recognition through exposure to vast amounts of data. Deep learning allows for continual improvement in the ability to recognize and process that information.

4. What are some examples of AI?

AI technologies are developing in many different fields with many diverse applications, some used today include:

  • Computer Vision – Recognizing objects in the visual world — to provide data input for control of an autonomous car, for example.
  • Speech Recognition – ‘listening’ to audio and interpreting what the words spoken are/reacting to them.
  • Natural Language Processing – Taking sequences of words and trying to determine the intended meaning.

Questions about Anthronoetic AI?

If you have additional questions or comments on Oceanit’s NoME anthronoetic AI, please contact us!

About Us

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