Algorithm Conference

July 16 - 18, 2020

Austin, Texas

Should an AI be recognized as an inventor?

AI as inventor?

It is internationally accepted that a patent can only be granted to a human. But in the age of AI, if such a system invents something without directions from the human that wrote it, who should be recognized as the real inventor? The AI system? The human that wrote it? Or no patent may be issued.


That is the question facing patent authorities and lawmakers in Europe, Israel, UK, and the US, after Professor Ryan Abbott and colleagues filed two patent applications on behalf of Dabus AI, an AI system written by Dr Stephen Thaler. The patent applications, which are still pending, formed the basis of a Wall Street Journal article archived here.

Researchers, companies and developers, who are increasingly turning to AI systems for business and commercial purposes, are keeping a close watch on the outcome of the patent applications.


Both Prof Abbott and Dr Thaler are confirmed speakers at Algorithm Conference. So you’ll have the opportunity to hear directly from the key people involved in the status quo-challenging patent applications.

Meet the key players

Stephen Thaler

Stephen Thaler, PhD

President/CEO, Imagination Engines

Dr Thaler is the author of more than two dozen patents on generative AI. DABUS, his newest patent, is the focus of a global legal effort to credit AIs as inventors of the IP they create, and the basis for this Wall Street Journal article archived here.


A 15-year veteran of aerospace giant McDonnell Douglas, his work for the military includes novel electro-optical materials discovery as well as brilliant robotic control systems capable of self-originating Machiavellian tactics on the battlefield.


He has authored numerous papers based upon his patented neural network paradigms to model cognition, consciousness, and sentience. In the first of these works, Thaler offered highly controversial models of hallucination within the traumatized brain.


More recently he has suggested a compelling perspective on the close relationship between psychopathologies and creativity. Now, he has received a patent for a neural network methodology that allows scale up of connectionist architectures to trillions of computational neurons in order to create free-thinking, sentient synthetic entities.

Keynote: DABUS, The sentient algorithm

Two patent-worthy inventions have been autonomously conceived by machine intelligence. Unfortunately, examiners have rejected them as patents since they lack a “natural person” as their author.


The irony of these decisions is that the extensive generative neural system responsible for these notions, DABUS, is arguably conscious and sentient, the key features believed to distinguish humans from many other terrestrial life forms, as well as from the much larger inorganic world.


But DABUS is built upon the human plan, since it develops feelings for whatever it may be attentive to, either in its external environment or within its imagination. In this way, it savors its discoveries and inventions. Thus, it can develop a ‘first person’, subjective feel for its own cognition, the criterion philosophers typically use to disqualify computer algorithms as conscious.

Now functioning as an artificial inventor, DABUS can relish its self-originated concepts, in much the same way it can appreciate its non-seminal cognition, all the while inventing heightened significance to itself.


Herein, the case is made that DABUS, apart from other forms of generative AI, has attained the purest form of personhood, one without extraneous corporeal features and functions.


Key takeaways:

1. A brief description of the previous patents DABUS is built upon.

2. A high-level description of DABUS and how it works.
3. The computational approach used by DABUS to generate subjective feelings.
4. A description of how DABUS harnesses its feelings to generate ideas and interpret its world.
5. The correspondence between DABUS and human consciousness.
6. Accounts of how DABUS can misbehave, and why that isn’t so bad.

Robert Marks

Ryan Abbott, MD, JD, MTOM

Prof of Law/Health Sciences, University of Surrey

Ryan Abbott is a Professor of Law and Health Sciences at the University of Surrey School of Law (UK) and Adjunct Assistant Professor of Medicine at the David Geffen School of Medicine at UCLA. He has published widely on issues associated with law and technology, health law and IP in leading legal, medical, and scientific journals.


He has worked as General Counsel and Medical Director of a mid-stage biotechnology company, and has been Of Counsel at law firms where he specialized in transactional matters and IP litigation for pharmaceutical and medical device companies.


He is a licensed physician, patent attorney, and acupuncturist in the United States, and a solicitor (non-practicing) in England and Wales. He is board-certified by the American Board of Legal Medicine.


Prof Abbott and a group of patent attorneys from the US, UK, Germany and Israel, have filed patent applications in the US, UK, Europe and Israel for a couple of inventions created by Dabus, the AI system designed by Dr Thaler.


Keynote: TBA

Discussion panel

Even if the patent applications for DABUS are rejected, the issue of whether generative AI systems should be recognized as inventors is not going away. Sooner or later, it’s an issue that policymakers will have to address.


For our part, the discussion panel –  Should an AI system be recognized as an inventor – offers a forum for the stakeholders involved in ongoing or rejected patent applications to share their experiences and for you to learn what the future holds in this domain. 

Prof Abbott and Dr Thaler, with two to three others yet to be announced, will take part in that discussion panel. You won’t want to miss out, so get your ticket, if you haven’t already.

The military & lethal AI

Can an AI system be sentient, creative and capable of understanding? Does the (US) military have an obligation to develop weapons systems with AI capabilities? Those questions are at the core of a keynote by Prof Marks. Click the button to learn more.


To register for a workshop and for the conference itself, click on that big red button.

Recommended activities for developers

Day 1: Workshop 1

How to curate quality datasets for machine learning

Thursday, July 16, 2020 (8 a.m. - 10 a.m.)

Day 1: Workshop 2

Exploring machine learning on the edge

Thursday, July 16, 2020 (1:30 p.m. - 5 p.m.)

Day 2 to Day 3

General conference sessions

Friday, July 17 - Saturday, July 18, 2020

Recommended activities for all

Day 1 Special

VIP Dinner

Thursday, July 16, 2020 (7 p.m. - 9:30 p.m.)

Day 2 all sessions

General conference sessions

Friday, July 17, 2020

Day 3 all sessions

General conference sessions

Saturday, July 18, 2020


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