Artists may need to start competing with -- or embracing -- computer-made songs and soundtracks in the near future, if a new AI music generator shows any indication of what could come next for the music industry. Researchers at artificial intelligence lab OpenAI have released Jukebox, an open-source algorithm that can generate music, complete with lyrics, vocals, and a soundtrack. All the algorithm needs is a genre, an artist, and a snippet of lyrics, and Jukebox can create song samples that can be realistic and quite catchy. OpenAI's music generator runs on the same sort of machine-learning technology used to create deepfakes and employed by the slew of sites that popped up in 2019 generating fake memes, fake Airbnb listings, and fake cats. Jukebox produces its AI creations using artificial neural networks that train a computer to learn from an influx of data.

Machine learning, as it is known, is the main driving force behind AI.

Crowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained annotators for each data instance which are then combined to estimate the ground truth. Further, annotations for constructs such as affect are often multi-dimensional with annotators rating multiple dimensions, such as valence and arousal, for each instance. Most annotation fusion schemes however ignore this aspect and model each dimension separately. In this work we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates. The model we propose is applicable to both global and time series annotation fusion problems and treats the ground truth as a latent variable distorted by the annotators. The model parameters are estimated using the Expectation-Maximization algorithm and we evaluate its performance using synthetic data and real emotion corpora as well as on an artificial task with human annotations

To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.

Nothing beats the feeling of implementing a model and training it to the point that it has a high level of accuracy and efficient performance when used on test data (bonus point if your model is small enough to work on edge devices). Not to take anything away from ML practitioners, but customers of commercial product do not utilize your models directly. In fact, they are most likely oblivious to the fact that there's is some form of machine learning involved in the product. And this shouldn't be a surprise. The ML techniques behind Google's suite of tools or Netflix personalization system are not blatantly exposed. There tend to be several interfaces that create an abstraction of the complexity of products.

In the last decade there has been a massive change in the marketing industry. After social media has grown dramatically on the Internet, social media marketers followed a progressive path. But making this change is no longer considered innovative. Why? Artificial intelligence has entered. AI is here to benefit both social media users and entrepreneurs.

An anti-Trump political organization is using AI originally designed to tackle Islamic State propaganda to counter coronavirus disinformation spread by the president. The system has been repurposed to spot comments from Trump that are about to go viral. It will then identify the most popular counter-narratives, and invite a network of more than 3.4 million influencers "to share these highly visual and emotional narratives from real people in unison and at scale." The initiative is being led by Defeat Disinfo, a political action committee (PAC) advised by retired general Stanley McChrystal, who commanded US and NATO forces during the Afghanistan war. The PAC claims that it won't use any bots, sock puppets, or false information.

One evening last October, the artificial intelligence researcher Gary Marcus was amusing himself on his iPhone by making a state-of-the-art neural network look stupid. Marcus' target, a deep learning network called GPT-2, had recently become famous for its uncanny ability to generate plausible-sounding English prose with just a sentence or two of prompting. When journalists at The Guardian fed it text from a report on Brexit, GPT-2 wrote entire newspaper-style paragraphs, complete with convincing political and geographic references. Marcus, a prominent critic of AI hype, gave the neural network a pop quiz. Surely a system smart enough to contribute to The New Yorker would have no trouble completing the sentence with the obvious word, "fire."

Renowned Visual Effects industry veteran Helena Packer, currently marking her 30th anniversary year working within the VFX arena, is currently working to enhance the next era of the visual effects field by developing new tools which will utilize the powerful advancements in digital technologies offered by Artificial Intelligence (AI). Packer explains how her new AI path came into play: In 2018, Raja Koduri, Chief Architect at Intel, approached her, asking if she would like to consult with Intel on the company's research and development of AI. For Packer, it felt like a natural fit, as she has been working to bridge technology with art throughout her entire career. During the past two years, Packer has had the opportunity to collaborate with some of the best minds currently working in AI, including Jason Yang, Co-Founder & CTO at Dgene. Packer, who sits on the Executive Committee of The Academy of Motion Picture Arts and Sciences (AMPAS), and also serves as Chair of the Diversity Committee for AMPAS' VFX Branch, is presently seeking new ways to make content creation easier and more gratifying through the incorporation of AI. "On the professional level, there is, at the moment, a huge surge in content production," Packer says.