Workers at some of the world's leading AI companies harbor significant concerns about the safety of their work and the incentives driving their leadership, a report published on Monday claimed. The report, commissioned by the State Department and written by employees of the company Gladstone AI, makes several recommendations for how the U.S. should respond to what it argues are significant national security risks posed by advanced AI. Read More: Exclusive: U.S. Must Move'Decisively' To Avert'Extinction-Level' Threat from AI, Government-Commissioned Report Says The report's authors spoke with more than 200 experts for the report, including employees at OpenAI, Google DeepMind, Meta and Anthropic--leading AI labs that are all working towards "artificial general intelligence," a hypothetical technology that could perform most tasks at or above the level of a human. The authors shared excerpts of concerns that employees from some of these labs shared with them privately, without naming the individuals or the specific company that they work for. OpenAI, Google, Meta and Anthropic did not immediately respond to requests for comment. "We have served, through this project, as a de-facto clearing house for the concerns of frontier researchers who are not convinced that the default trajectory of their organizations would avoid catastrophic outcomes," Jeremie Harris, the CEO of Gladstone and one of the authors of the report, tells TIME. One individual at an unspecified AI lab shared worries with the report's authors that the lab has what the report characterized as a "lax approach to safety" stemming from a desire to not slow down the lab's work to build more powerful systems.

The first Artificial Intelligence ever created was Pandora, a creation of the Greek gods Hephaestus and Athena. Pandora means both "all-gifted" and "all-giving" and was constructed with attributes from every other Olympian god. Pandora was created at the request of Zeus, the king of the gods, as a punishment for Prometheus stealing fire, the first disruptive technology, and handing it over to mankind. Zeus punishes Prometheus, the proto-god of technology and intelligence, for giving fire to mankind by chaining him to a rock, and he punishes mankind by giving them Pandora, the first A.I., who has been endowed with attributes from all of the gods. But how is this gift a punishment?

Artificial intelligence is the future, but it already has a prominent standing in the present. As data science gets more sophisticated and consumers continue to demand a more personalized customer experience, AI is the tool that will help enterprises better understand their customers and audiences. But even though AI has all the potential in the world, if we cannot figure out how to address the ethical challenges that remain, this full potential may never be reached. As this technology evolves, one question should remain in the minds of all leaders seeking to implement an AI strategy: How can I ethically and responsibly make the most of AI within my organization? About seven years ago, Gartner released what they referred to as the "Hype Cycle for Emerging Technologies," which highlighted the technologies it predicted would change society and business over the next decade.

Today's organizations need to pursue digital transformation to survive in the future, consulting firm Baker Tilley noted in a blog post. "Digital transformation is critical to preparing yourself for what's next." Technology leaders had approximately five times the revenue growth of the laggards, displaying the rising importance of adopting technology in the post-pandemic economic climate, Baker Tilley added. "But digital transformation goes beyond just implementing new tools and technology," the blog post stated. "By conducting a financial assessment, creating a business case and building a road map, your organization can make the financial case for your digital transformation."

Critical technology, such as artificial intelligence (AI) and machine learning, has already defined our world and will increasingly do so in the future. In this context, we must remember that technology is neither positive nor negative but rather a reflection of its curation. This notion creates a responsibility that our leaders - as the curators - must guide its development, implementation and accessibility in a way that is reflective of the values of the society we wish to create, not simply a projection and perpetuation of the realities and biases of our history. The responsibility for leaders is to face the exponential and transformative potential of technology and leverage it for the betterment of society - while proactively overcoming the risks presented. Innovators worldwide are showing that increased equality, and equity, is possible through harnessing the power of emerging technologies.

AI and ML strategies require foresight and planning--they shouldn't be an afterthought for your organization. Here are four best practices to help capital markets adopt and benefit from modern AI/ML technologies. When introducing new AI/ML strategies, IT leaders must ensure that they integrate and fit with existing modernization efforts, as opposed to being a bolt-on afterthought. This will lead to a true integration of AI/ML and business. In capital markets, the stakes have been raised for participants to establish value, win loyalty, and expand their share of wallet.

To tackle the challenge of how to distribute organs, vaccines, and other kinds of health care, organizations are relying on AI and analytics. But many of them treat ethical considerations as an afterthought. Such factors should be taken into the account at the outset of the effort to create the AI algorithm or analytics model. Health care organizations, like many other enterprises, face steep challenges in their attempt to maximize operational efficiency in the face of resource constraints. Whether it is a hospital's attempt to optimize staffing or a government trying to fairly allocate and distribute limited doses of Covid-19 vaccines, these tasks can be formidable.

As more and more businesses become fully digitized, the instantiation of their business processes and business capabilities becomes software based. Any software implementation involves decisions being made which can result in a business getting stuck in time or creating a basis for business differentiation. A lot of these decisions have to do with simple things like what fields are made operational and what functions get implemented or not. Several years ago, I was involved in helping an insurance company with its analytics software implementation. Everyone on the management team wanted the analytics software completed so they could improve their business, but one of the project leads wanted the analytics task completed after an upgrade to their key transactional processing software.

As the size of the NLP model increases into the hundreds of billions of parameters, so does the importance of being able to create more compact representations of these models. Knowledge distillation has successfully enabled this but is still considered an afterthought when designing the teacher models. This probably reduces the effectiveness of the distillation, leaving potential performance improvements for the student on the table. Further, the difficulties in fine-tuning small student models after the initial distillation, without degrading their performance, requires us to both pre-train and fine-tune the teachers on the tasks we want the student to be able to perform. Training a student model through knowledge distillation will, therefore, require more training compared to only training the teacher, which limits the benefits of a student model to inference-time.

In this interview, Salvatore Sinno, Chief Security Architect and Director of Cybersecurity Innovation at Unisys speaks to Open Access Government about how he defines digital transformation and where he sees it heading in the future. He also explains how disruptive technologies, including cloud, artificial intelligence (AI) and blockchain promise to transform the way businesses operate and serve customers. Also, Salvatore explains how digital transformations work when they are well-planned and executed when security is not an afterthought, and the business impact is steady and sustainable over the long-term. In light of the UK under lockdown, we also learn about the extent to which many Brits have been forced to embrace digital technologies like the older generation. Finally, on security, Salvatore details some of the firm's findings concerning how digital risk falls dramatically during the pandemic, including online shopping and cybercrimes.