As artificial intelligence plays an increasingly important role in the R&D process, the premise that invention is a uniquely human characteristic is being challenged. Patent offices and courts around the world have recently been grappling with the question of whether an AI system can be the inventor of a patent. This has been prompted by Dr. Stephen Thaler's applications to designate his AI system (known as'DABUS') as the inventor of patents filed in multiple jurisdictions. Most recently, the appeal board of the European Patent Office (EPO) refused Dr. Thaler's patent applications because there was no valid inventor. Dr. Thaler, as part of the Artificial Inventor Project, is pursuing parallel patent applications across over fifteen jurisdictions which designate his AI system, DABUS, as the inventor.
The US is the key market for artificial intelligence platforms market in North America. The US and Canada together hold approximately 26% of all global AI-related patent applications, while IBM has the largest share of AI-related patents, followed by Microsoft and Google. Market growth in this region will be slower than the growth of the market in APAC, Europe, and MEA. The need for automated machine-driven decisions will facilitate the artificial intelligence platform's market growth in North America over the forecast period. The artificial intelligence platforms market is set to grow by USD 17.29 billion at a CAGR of 35% from 2020 to 2025, according to the latest research report from Technavio.
The uprising of Artificial Intelligence machines (hereinafter referred as "AI") is a popular and intriguing subject for many science fiction works. The advancement of AI machines and their progression with respect to playing a significant role in our lives has increased exponentially in the past few years. The future possibilities of this technology has stirred a hornets' nest of innumerable possibilities. As we witness AI machines overlapping with Intellectual Property Rights (IPR), it gives rise to many questions concerning legal discipline. When the earliest substantial work in the field of Artificial Intelligence was concluded in the mid-20th century by the British logician and computer pioneer, Alan Mathison Turing, nobody could have imagined that there will be an attempt towards an assimilation of technical solutions created by an AI machines into the scope of patent law.
In public proceedings, the Legal Board of Appeal of the EPO confirmed that under the European Patent Convention (EPC), an inventor designated in a patent application must be a human being. This was the judgement in combined cases J 8/20 and J 9/20, where the board just dismissed the applicant's appeal. Here, both the applications were made by a Missouri physicist Stephen Thaler, whose AI-system DABUS had made the inventions. Device for the Autonomous Bootstrapping of Unified Sentience, or DABUS, is a computer system programmed to invent by itself. It is, basically, a swarm of disconnected neutral nets that can continuously generate thought processes and even memories that can, over time, generate new and inventive outputs independently.
The enormous advantages of AI have a prominent impact on video conferencing, webcasting, and live streaming applications. Deemed growth of digital technology and the rapid evolution of digital platforms unfolded never-ending demands to meet end-user perceptions. Satisfying these needs will help the webcasting and live streaming platforms into a fully automated solution with the utmost competitive advantage. But the question for many of the service providers is how to integrate AI to this solution that can scale and automate according to user needs. The live streaming & Webcasting market is developing like never before.
Artificial intelligence (AI) has had a profound impact on our society in recent years, but it's been around longer than you may realize. Many people attribute the beginning of AI to a paper written in 1950 by Alan Turing titled "Computer Machinery and Intelligence." The term artificial intelligence, however, was first coined in 1956 at a conference that took place at Dartmouth College in Hanover, New Hampshire. Since then, interest in AI has wavered. Its most recent resurgence can be attributed to IBM's Deep Blue chess-playing supercomputer and its question-answering machine Watson. Today, AI is part of our everyday lives – from facial recognition technology and ride-share apps to smart assistants.
Description: *** Cannot provide sponsorship upon conversion. What is the specific title of the position? Lead Data and AI Engineer Work location? Preferred Locations - MA or MN (Client facilities). 100% telecommute is also considered. Work hours (ex. 9am-5pm day/night shifts rotating shifts etc)? 9-5 Please provide a summary of the project/initiative that this candidate will be working on? We are establishing Agile Data Warehouse in cloud and many new AI practices to enable personalization in various capabilities to improve employee experience. Please describe the team the candidate will be working with - how many members? 10 – 12 team members What is the break-down of the teams skill sets (ex: 1 PM 4 Developers etc.)? 1 PM, 3 Product owners 2 Sprint teams consisting - 1 Scrum Masters and 12 developers What are the top 5-10 responsibilities for this position (please be detailed as to what the candidate is expected to do or complete on a daily basis)? • Identify opportunities for Data Engineering and AI to enhance the core product platform, select the best machine learning techniques to the specific business problem and then build the models that solve the problem. • Architect and design AI/ML and Analytics solutions and cloud services • Own the end-end process, from recognizing the problem to implementing the solution. • Establish DataOps and MLOps principles and best practices What does the ideal candidate background look like (ex: healthcare specific background specific industry experience etc.)? a. Hands on experience with modern application – microservices, Cloud and CI/CD b. 5-7 years of hands on Data and AI engineering work c. Good communication with developing architecture and design documentation What skills/attributes are required (please be detailed as to number of years of experience for each skill)? • Bachelor's Degree or master's degree in Computer Science. • 5+ years of hands-on software engineering experience. • Demonstrated AI/ML solution design experience • Proven work experience in Spark, Python, SQL, Any RDBMS. • Familiarity with Azure Data Lake, Synapse, ADF, Power BI. • Experience building, deploying and maintaining ML models in production • Experience with MLOps tools such as ModelDB, MLFlow and Kubeflow. • Familiar with best practices in the data engineering and MLOps community. • Ability to convey complex concepts and ideas in a clear and concise manner to a wide range of audience internal business stakeholders, outside partners and technology teams. • To be able to work in a fast-paced agile development environment. • Proven track record in working with diverse teams to achieve goals • Strong problem solving and troubleshooting skills with the ability to exercise mature judgment. What skills/attributes are preferred (what will set a candidate apart)? • Experience with AzureML • Expert in Azure Synapse, Azure Container Registry, Azure App services Of the required skills listed, which would you consider the top 3? Please list your expectations regarding years of experience for each requirement. a. AI/ ML Solution design b. Strong problem solving and troubleshooting skills with the ability to exercise mature judgment. c. MLOps What will the interview process look like? (Video phone or in person? How many rounds? How technical will the interviews be?) a. How many rounds? 2-3 b. Video vs. phone? Video c. How technical will the interviews be? Mostly technical
It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.