SEMINAL AI:

Predictive AI:
Text to Future Novel Markets

Powered by: Open AI
GPT-4

SEMINAL is a Predictive AI that uses the GPT-4 Language Model to mine and analyze data from the past and present to predict when future "Novel Markets" will actualize. Our approach ensures agencies to prioritize certain methods, target more profitable sectors, and allocate resources to develop higher-yielding start ups.

GPT-3 Powered Predictive Models

“Since the 1990s, the growth in novel patents — those that mention a new technology — has been negative in the United States. According to one widely cited study, U.S. research productivity declines 50% every 13 years, largely because new ideas are drying up. The implication is that the U.S. needs to double its research investment about every dozen years just to stand still in growth terms.”
-MIT Sloan Management Review

PROBLEM

Currently, High Risk Return on Investments plagues the Venture Capital Business Model. While larger firms are able to recover from misguided investments, smaller venture firms are unable to compete. The result is a clogged "ideation" pipeline due to a handful of firms who are able to gainfully participate.

Venture/Angel Investment Hit Miss success rate for investments

Current Business Model is not Efficient or Fiscally Responsible

Institutional Partners bare the brunt

Smaller Venture Funds require higher return models to compete

SOLUTION

By maximizing the power of AI GPT-4 language search along with a our Proprietary Prompt Engieneering Method, SEMINAL AI will provide real time analytics for novel markets to VC firms. We envision a revised Venture Capital Model allowing for Efficent Return on Investment.

Pillars of Technology Growth
Global Connectivity

The speed by which technology is initited and transferred is determed by both contained and spontainous variables. SEMINAL AI will address each output as a unique and relatable input for predictable outputs.

AI faster and stronger every 3-4 months. Computing systems every 2-3 years

Semiconductors smaller, faster, ubiquitous in everyday products
IoT: Every 2-3 years

Consumer Regulated Economy
Markets will Evolve for Specific Industries

Pandemic Worldwide Quarantine:
Unpredictable Spontaneous Irreversible Policy

Culture: Behavior Patterns of Society that Become New Norm

Patents: Filed up to 15 -10 years prior actaulization.

Wireless Generation Network Upgrades:
Every 5-6 Years

Academic Publications:
Peer to Peer R&D for future commercialization

Consumer Adoption

SEMINAL AI takes into consideration Human Behavior as a major contributor to Consumer Adoption. How and Why a Product or Service becomes Viral is dependent upon the 4 Corners Adoption.

TOOLS:
How we make our world more useful.

COORDINATION:
How we get things done.

CONVERSATION:
How we connect with each other.

EMOTION:
How we know what to care about.

Types of Ideation

“Idea generation is fueled by consumption (things), not creativity. Creativity is just connecting things. The more “things” you have to connect, the better your ideas will become. Quality and Quantity of things consumed are a crucial factoring ability to generate seminal ideas.”
-Steve Jobs

Problem
Solution:

This is the most simple method of progress, where someone has found a problem and as a result, solves it.

Derivative
Ideations:

This involves taking something that already exists and changing it. Think: FASTER/STRONGER.

Symbiotic
Ideations:

A symbiotic method of idea creation is when multiple ideas are combined, using different elements of each to make a whole.

Revolutionary Ideations

A revolutionary idea breaks away from traditional thought and creates a brand new perspective. For example, the writings of Copernicus (a development of classical Greek thought).

Serendipitous
Discovery:

Serendipitous solutions are ideas which have been coincidentally developed without the intention of the inventor. For example, the discovery of penicillin.

Targeted
Innovation:

Creating a targeted innovation deals with a direct path of discovery. This is often accompanied by intensive research in order to have a distinct and almost expected resolution. For example, linear programming.

Artistic
Innovation:

Artistic innovation that disregards the necessity for practicality and holds no constraints.

Philosophical
Ideations:

The philosophical idea lives in the mind of the creator and can never be proven. This type of idea however can still have vast residual effects. For example, the idea of eternal recurrence.

Ideation Perpectives

Innate v. Social

The purpose of Innovation is to Pronounce and Solve Problems. SEMINAL AI Categories these conditions and applies them to our Prompt Engineering Protocols. Innate and Social Perspectives are the Baseline of Novel Invention.

Innate Perspective

Feelings of fear, greed, love, ego, ambition and necessity all play a role in this process but fundamentally we innovate to solve problems. That urge to do something, to move the narrative of human progress forward by trying new things, is the spirit of innovation.

Social Perpective

Ideas often originate from dialogues in which an individual hears about a challenge and recognizes a new path for solving it. It is therefore crucial to create a space in which challenges are discussed openly and without fear, stimulating new solutions.

GPT-4

“Generative Pre-trained Transformer” or “GPT” is essentially a string of language processing models that evolve and learn through AI. This machine learning model can generate new text using data from the internet and other databases. GPT-4 is a text-only model in a landscape where multimodal AI is becoming increasingly popular. In short, more independant data sets are explored for maximum outout.

GPT-3 Predictive Model Uses

Prompt Engineering:
Novel Ideation

Prompt engineering is the process of training prompts/inputs which yield useful or desired results from a data set. In short, it's like a Google Search input for AI systems. As a rule of thumb while designing the training prompt you should aim toward getting a zero-shot response from the model. SEMINAL AI uses a Proprietary Prompt Engineering Methodology to ensure the most accurate novel models in real-time.

Level 1:
Exposition of Barriers and Gateways to Market

Level 2:
Input Unique Derivative and Seminal Scenario Values

Level 3:
Identify Spatial and Temporal Extrapolating Conditions

Level 4:
Apply Machine Learning Quants to Outcomes of Multiple Variants

Level 5:
Compare / Contrast Supervised v. Unsupervised Learning Outcomes

Level 6:
Modulate Outcomes as Derivative Ideations

Level 7:
Repeat Process with Subversive Derivative Scenarios

Level 8:
Identify and Eliminate Derivative Redundancies

Level 9:
Gamma Inception / Seminal Ideation Formed