Unlocking Performance Potential

Marcello Cini, a distinguished Italian physics and epistemiology academic, wrote in 1968, seven years before 'Apple I' was built: "I am quite convinced that in the next twenty or thirty years we will see a development in the computer industry resulting from the increase in private computer consumption, exactly analogous to what private automobile consumption has been [...]. This development will introduce forms of further selection, further subservience, further competition, and imprisonment of man in an increasingly relentless logic, mainly due to private consumption. It is clear that, if, from an economic point of view, this is an industry that can truly give development to the system entirely analogous to that of private motorization, it risk enslaving the individual in a consumption that narcotizes him..."

Almost 60 years later that very same prophecy resounds truer than ever. AI, immersive reality interfaces, and next generation computing and connectivity are becoming so pervasive that they have the potential to give humans super powers, re-imagine economies, and make government bureaucracies invisible. But they are also forcing society to reflect on the consequences in terms of job replacement , misinformation , mental health and more.

This presentation will argue that societies should to shape a future where technology innovation is used not only to increase productivity, but also to address grand challenges, such as climate change, migration, water conservation, equal access to health and education. A future where technology innovation is developed and used respecting human values, like dignity, personal achievement, appreciation, equity, openness, and empathy.

This human centric journey can only be achieved through collaboration across public sector, private sector, people and communities. A collaboration founded not on delegating or abstaining from making decisions, but on every individual and every organization acting responsibly - “I think individual abstinence is not a solution to exploitative practices. A company’s policy change should be the response” said Arvind Narayanan, director of Princeton’s Center for Information Technology Policy .

Enterprises, particularly technology companies at the center of the revolution, that embrace this ambitious human-centric vision, will not only contribute addressing societal challenges, but also push the frontier of innovation gaining competitive advantage and building new markets .

Many organizations today still use decades-old technology to secure their networks - VPNs and legacy firewalls were not built for the world of cloud computing, SaaS applications, or remote work. Meanwhile, malicious actors have become increasingly sophisticated and are taking advantage of organizations' ever-expanding attack surfaces. In this presentation, we will introduce the Zero Trust security framework and discuss how this network architecture is an imperative for the future of cybersecurity.

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Cybersecurity Norms and Trends: How Does Your Business Stack Up?

There's plenty of talk about the cybersecurity best practices businesses should embrace, but how many actually embrace them? What are the major gaps in enterprise cybersecurity today, and what does it take to set your business apart from the crowd in a good way?

Learn the answers in this workshops which highlights key cybersecurity trends and gaps based on findings from IDC's Cybersecurity Capabilities Assessment Framework survey. IDC analysts will also offer guidance on how to overcome roadblocks that hamper adoption of more effective cybersecurity practices for the typical business.

 Key takeaways from this webinar will include:

• Gain insights into the prevalent gaps in enterprise cybersecurity today

• Explore key cybersecurity trends, including emerging threats, technologies, and strategies in the cybersecurity landscape

• Get actionable guidance to enhance your organization's cybersecurity posture

Every machine learning system is designed by humans to achieve certain goals. These may be as benign as identifying cats and dogs in a dataset of pictures (for the sake of producing better search results) and as consequential as predicting whether a defendant is likely to commit another crime in the future (when deciding on early, conditional release from prison). And there is a long path between defining an overall objective and calibrating the system so that it delivers desirable outcomes. Even in black-boxed systems, testing and calibration is done by humans. With this power comes huge responsibility. Are engineers and data scientists aware of how their "technical" decisions may impact end-users and us as a society? How can we work together, in various roles, to ensure social accountability of ML/AI systems, not just for their (inevitable) mistakes but also for the trade-offs that are most consequential?