There’s a point in your life when you realize that the people you once looked up to just aren’t as perfect as you once imagined them to be. We’re now struggling to handle that realization with our intelligence. In a world where a moon launch, once the pinnacle of human achievement, can now be handled by a fraction of the computing power found in your smartphone (a transformation that happened in less than a century), we can’t help but wonder about where we’re headed in the future – a future where humans may not even be relevant anymore. But don’t take my word for it, Elon Musk even made a documentary on it, and it’s one of the reasons why he got involved with Neuralink. The future isn’t here yet, though, and there’s still time for us as a civilization to find ways to keep ourselves relevant – particularly in regards to our cognitive relevance, which is what got us here in the first place.
The singularity is near, or maybe we’re already in it. Whatever the case is, machine learning and big data will have a tremendous influence on our society. The machine minds are coming online, and you had better learn to adapt if you want to succeed. But what are big data and machine learning? Keep reading to find out.
Imagine being able to walk into a strip-mall and have thousands of microscopically-fine electrodes inserted into your brain, all implanted as quickly and as efficiently as if you were having LASIK eye surgery, and designed to boost your brain from a simple smartphone app.
Recently, OpenAI’s Amanda Askell, Miles Brundage, and Jack Clark joined Rob Wiblin on the 80,000 hours podcast to discuss a wide range of topics related to AI philosophy. policy, and publication norms. During the conversation, they also discussed where to start if you’re trying to understand AI and AI policy. It was a topic that spoke to me directly, since I’m interested in the field but totally overwhelmed by the resources (or lack thereof) that are available.
Article: All Hail the Algorithm
A five-part series exploring the impact of algorithms on our everyday lives
Data Science is on the agenda but what about Data Science Ethics? The twin motors of data and information technology are driving innovation forward in most every aspect of human enterprise. In a similar fashion, Data Science today profoundly influences how business is done in fields as diverse as the life sciences, smart cities, and transportation. As cogent as these directions have become, the dangers of data science without ethical considerations is as equally apparent – whether it be the protection of personally identifiable data, implicit bias in automated decision-making, the illusion of free choice in psychographics, the social impacts of automation, or the apparent divorce of truth and trust in virtual communication. Justifying the need for focus on the Data Science Ethics goes beyond a balance sheet of these opportunities and challenges, for the practice of data science challenges our perceptions of what it means to be human.
While rich medical, behavioral, and socio-demographic data are key to modern data-driven research, their collection and use raise legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes. Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model.
The future of work isn’t something that happens to you – it’s something you create for your company and your own career. Unfortunately, C-level technology and business leaders are often uncertain on how to do it. We’ve just released a major new report, ‘The Adaptive Workforce Will Drive The Future Of Work,’ to establish a North Star for your aspirations – and a blueprint for how to get there.