Collaborating Authors

The Future

AI with purpose: Ireland's vision for artificial intelligence


The Irish Government released its AI Strategy "AI - Here for Good" in July 2021, outlining a set of initiatives to meet to be an international leader in using AI to benefit our economy and society. It is a positive step towards further materialising the significant benefits from AI and associated technologies. Ireland is well positioned to be a leader in AI thanks to its innovative culture, adaptable capability, and tech savvy workforce. Adopting AI across the enterprise is a critical component of realising our national ambition. It is within organisations and businesses where the theoretical capability of AI is combined with industry knowledge to create powerful solutions, which can result in a meaningful impact to our economy, our society and our lives.

Artificial Intelligence (7 weeks)


This course explores the idea of artificial intelligence (A.I.) from three different perspectives: scientific, philosophical, and cultural. The scientific perspective provides insight as to how artificial intelligence technologies work, the current limitations, and supposed future potential. The philosophical perspective explores whether A.I. is good or bad, essential or dangerous, and what the future could hold. The cultural angle examines how society views A.I. and whether these views are accurate. Toward the end of the course deeper topics will be introduced including how A.I. compares to human intelligence, the singularity, and futurism.

The New Poem-Making Machinery

The New Yorker

I met Dan Selsam when we were toddlers. He liked solving math problems. We both liked the show "ThunderCats." I became a comedy writer. Dan became a computer scientist.

The Promise of AI: Opportunities and Obstacles


I mean, there's certainly, I can talk about the motivation, you know, coming out of government, people were asking me to write about my experience. And then it's taken me a few years to try to formulate my thoughts enough that it would be worth reading, you know, two years from now, three years from now, which is pretty hard in a fast moving area such as the one we're talking about. The greater theme, that it seems to represent a future that I think will be relevant 10 or even 20 years from now, is one of composability. This is a notion that we can expand infinitely in any direction. It's a framework that I think describes our digital world better than any other that I've come up with.

SentinelOne Unveils Skylight to Power Machine-Speed XDR


SentinelOne, an autonomous cybersecurity platform company, unveiled SentinelOne Skylight. Skylight provides full data visibility, ingestion, and storage capabilities, integrating SentinelOne and third-party data within SentinelOne Storylines. With data and context in one place – beyond the endpoint – security teams are empowered to make better decisions, automate workflows, and derive more value from existing technology and security tools. An evolution of the Singularity XDR platform, Skylight delivers on SentinelOne's commitment to a holistic approach to cybersecurity, arming security teams with the power of machine-speed technology. Building upon DataSet's ability to ingest, correlate, search, and action data from any source, SentinelOne Skylight enables security teams to observe and hunt across all security events for increased efficiency.

The Future of AI in Primary Care


Founded in 2020, WellAI, an AI health-tech company, is the developer of scientifically and technologically advanced medical applications. WellAI's engineers, fresh off the development of a COVID-19 research tool (presented at the IFCC annual conference) leveraged their expertise into developing an advanced clinical diagnostic tool (triage solution) for physicians, caregivers, and employees/individuals. The company is the developer of the Digital Health Triage Assistant, WellAI for Medical Providers, and the Adaptive AI Diagnostic Engine. It also provides custom solutions. The AI Diagnostic Engine has uniquely assimilated 30 million medical studies and has the ability to diagnose, with 83% average accuracy, more than 500 health conditions including pediatric specific conditions using simple spoken language in less than 1 minute.

Elon Musk, $500,000 says you're wrong about the future of A.I.


The authors believe Musk is overstating what “artificial general intelligence” can do—and they’re calling him on it with a little wager.

The New Generation of A.I. Apps Could Make Writers and Artists Obsolete


For decades we've been warned that artificial intelligence is coming for our jobs. Sci-fi books and movies going all the way back to Kurt Vonnegut's Player Piano portray a world where workers have been replaced by machines (or in some instances, just one machine). More recently, these ideas have moved from the annals of novels into the predictive economic papers of governments and consulting firms. In 2016, the Obama administration authored a report warning that the robots were coming, and that millions of Americans could soon be out of a job. In 2021, McKinsey predicted that algorithms and androids would vaporize 45 million jobs by 2030.

The Future of artificial intelligence Technology


The future of AI is so bright that it can no longer be ignored! The potential of the technology is tremendous and it will be a major part of our lives in 2018. We cannot think of how powerful it is, that we've only just scratched the surface. And there are still so many opportunities to take advantage of. In this post, I'm going to cover what we need to know about artificial intelligence technology and some of its benefits and limitations.

La veille de la cybersécurité


Explanation methods that help users determine whether to trust machine-learning model predictions can be less accurate for disadvantaged subgroups, a new study finds. When the stakes are high, machine-learning models are sometimes used to aid human decision-makers. For instance, a model could predict which law school applicants are most likely to pass the bar exam to help an admissions officer determine which students should be accepted. These models often have millions of parameters, so how they make predictions is nearly impossible for researchers to fully understand, let alone an admissions officer with no machine-learning experience. Researchers sometimes employ explanation methods that mimic a larger model by creating simple approximations of its predictions.