Every aspect of life can be guided by artificial intelligence algorithms – from choosing what route to take for your morning commute, to deciding whom to take on a date, to complex legal and judicial matters such as predictive policing.
Big tech companies like Google and Facebook use AI to obtain insights on their gargantuan trove of detailed customer data. This allows them monetize users’ collective preferences through practices such as micro-targeting, a strategy used by advertisers to narrowly target specific sets of users.
In parallel, many people now trust platforms and algorithms more than their own governments and civic society. An October 2018 study suggested that people demonstrate “algorithm appreciation,” to the extent that they would rely on advice more when they think it is from an algorithm than from a human. Read More
Daily Archives: April 17, 2019
The 50 shades of emotion AI
Artificial intelligence offers us an opportunity to amplify service and the integration of technology in everyday lives many times over. But until very recently, there remained a significant barrier in how sophisticated the technology could be. Without a complete understanding of emotion in voice and how AI can capture and measure it, inanimate assistants (voice assistants, smart cars, robots and all AI with speech recognition capabilities) would continue to lack key components of a personality. This barrier makes it difficult for an AI assistant to fully understand and engage with a human operator the same way a human assistant would.
This is starting to change. Rapid advances in technology are enabling engineers to program these voice assistants with a better understanding of the emotions in someone’s voice and the behaviors associated with those emotions. The better we understand these nuances, the more agile and emotionally intelligent our AI systems will become. Read More
Discriminating Systems — Gender, Race, and Power in AI
There is a diversity crisis in the AI sector across gender and race.
The AI sector needs a profound shift in how it addresses the current diversity crisis.
The overwhelming focus on ‘women in tech’ is too narrow and likely to privilege white women over others.
Fixing the ‘pipeline’ won’t fix AI’s diversity problems.
The use of AI systems for the classification, detection, and prediction of race and gender is in urgent need of re-evaluation.
The diversity problem is not just about women. It’s about gender, race, and most fundamentally, about power.10 It affects how AI companies work, what products get built, who they are designed to serve, and who benefits from their development. Read More
The race is on: assessing the US-China Artificial Intelligence competition
Discussions of artificial intelligence are everywhere. Understandably so: AI has a seemingly limitless range applications, from schools to the battlefield. McKinsey & Company estimated that AI is likely to result in $13 trillion of additional global economic activity by 2030. AI also allows the development of autonomous weapons and novel platforms, such as advanced drone swarms. A revanchist Russia might be the scourge of the Western defense community, but Vladimir Putin has arguably issued the clearest articulation of AI’s massive potential: “Whoever becomes the leader in [AI] will become the ruler of the world.” But how do we assess who is leading?
A simple metaphor proves a powerful tool for thinking about the AI race: the traditional manufacturing process. Applying the analogy to the United States vs. China AI competition illustrates that although the United States is ahead overall, China is positioned to surpass it in the long term. On some measures, China is already winning.
A simple manufacturing process consists of three elements: raw materials, production, and manufactured goods. Raw materials are inputs such as wood, wool, or steel. Production includes the equipment, techniques, and manpower to process the raw materials. Manufactured goods are the final outputs: chairs, guns, and tanks. Likewise, current AI systems typically take large amounts of input data, process it using machine-learning techniques, and output trained algorithms. For example, numerous photos of cars can be processed using machine learning to create an algorithm that recognizes cars in other photos. The government and private sector use those algorithms in applications from autonomous vehicle vision to detecting terrorist activity. (Note: machine learning drives the current AI focus, but technically it is only one form of AI. Other forms are less reliant on data.) Read More
Finding enemy radars by Moonlight
Ever since radar proved a game-changer for air defence in World War II, increasingly advanced systems have enabled the accurate detection of enemy aircraft, ships, land systems and missiles and more. But adversaries have equivalent systems that pose a risk to military operations, and not knowing where they are could prove disastrous.
“So we created some business rules and put those manual verification and validation processes into software rules into our system which we’ve called Moonlight. Read More
Rick's Cafe AI 