In 2020, people benefit from artificial intelligence every day: music recommender systems, Google maps, Uber, and many more applications are powered with AI. One of popular Google search requests goes as follows: "are artificial intelligence and machine learning the same thing?". Let's clear things up: artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three different things. The term artificial intelligence was first used in 1956, at a computer science conference in Dartmouth. AI described an attempt to model how the human brain works and, based on this knowledge, create more advanced computers. The scientists expected that to understand how the human mind works and digitalize it shouldn't take too long.

But with machine learning, FPGAs are showing benefits beyond those of more conventional solutions.

In October 2017, Facebook altered the Instagram API to make it harder for users to search its giant database of photos. The change was a small element of the company's response to the Cambridge Analytica scandal, but it was a significant problem for parts of the digital marketing industry. Not long before, New York-based influencer marketing agency Amra & Elma had developed a platform that ingested data from Instagram, and allowed its client to use AI image classifiers to find very specific influencers. For instance, they could find an influencer with, say, between 10,000 and 50,000 followers who had posted photos of themselves in a Jeep. Facebook's move killed this capability in a keystroke.

Facebook groups are ripe targets for bad actors, for people who want to spread misleading, wrong or dangerous information. And in a recent opinion column in WIRED magazine, she and a co-author write that the company's so-called pivot to privacy, Facebook's promise to protect sensitive user information, did little to combat the spread of misinformation. Instead, Facebook encouraged users to join its groups, which are private pages for users with similar interests. We want to note here that Facebook is among NPR's recent financial supporters. Nina Jankowicz is here with us now.

Apple's latest Watch update encourage users to wash their hands properly by showing them a 20-second timer on their wrist while they are doing it. The firm announced the new feature at their annual Worldwide Developer Conference on Monday alongside a spate of other updates. Apple said washing hands properly for at least 20 seconds can help prevent the spread of illnesses, such as the deadly coronavirus that put the world in lockdown. It uses the motion sensors, microphones and machine learning to detect when someone starts washing their hands then initiates a 20-second countdown timer. Other new features announced for the wearable device include the ability to swap Watch faces, dance tracking in the fitness app and sleep monitoring.

Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. Most previous works focus on single-item diffusion. In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e.g., products, news stories) propagate in the same network and influence probabilities on edges are unknown. We adapt the combinatorial multi-armed bandit (CMAB) framework for the OCIM problem, but unlike the non-competitive setting, the important monotonicity property (influence spread increases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem. We prove that the Triggering Probability Modulated (TPM) condition for CMAB still holds, and then utilize the property of competitive diffusion to introduce a new offline oracle, and discuss how to implement this new oracle in various cases. We propose an OCIM-OIFU algorithm with such an oracle that achieves logarithmic regret. We also design an OCIM-ETC algorithm that has worse regret bound but requires less feedback and easier offline computation. Our experimental evaluations demonstrate the effectiveness of our algorithms.

We present a general computational approach that enables a machine to generate a dance for any input music. We encode intuitive, flexible heuristics for what a 'good' dance is: the structure of the dance should align with the structure of the music. This flexibility allows the agent to discover creative dances. Human studies show that participants find our dances to be more creative and inspiring compared to meaningful baselines. We also evaluate how perception of creativity changes based on different presentations of the dance. Our code is available at https://github.com/purvaten/feel-the-music.

Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and relations, or a satisfactory number of labeled sentences to train a competent relation extraction model. However, low resource relations are very common in KGs, and those newly added relations often do not have many known samples for training. In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. We propose a general framework called Weighted Relation Adversarial Network, which utilizes an adversarial procedure to help adapt knowledge/features learned from high resource relations to different but related low resource relations. Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations. Experimental results show that the proposed approach outperforms previous methods regarding low resource settings for both link prediction and relation extraction.