It's a world of opportunity rapidly pressuring organizations of all sizes to rapidly adopt technology to not just survive, but to thrive. And Andrew Dugan, chief technology officer at Lumen Technologies, sees proof in the company's own customer base, where "those organizations fared the best throughout covid were the ones that were prepared with their digital transformation." And that's been a common story this year. A 2018 McKinsey survey showed that well before the pandemic 92% of company leaders believed "their business model would not remain economically viable through digitization." This astounding statistic shows the necessity for organizations to start deploying new technologies, not just for the coming year, but for the coming Fourth Industrial Revolution. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. Lumen plans to play a key role in this preparation and execution: "We see the Fourth Industrial Revolution really transforming daily life ... And it's really driven by that availability and ubiquity of those smart devices." With the rapid evolution of smaller chips and devices, acquiring analyzing, and acting on the data becomes a critical priority for every company. But organizations must be prepared for this increasing onslaught of data.

Two menacing men stand next to a white van in a field, holding remote controls. They open the van's back doors, and the whining sound of quadcopter drones crescendos. They flip a switch, and the drones swarm out like bats from a cave. In a few seconds, we cut to a college classroom. The students scream in terror, trapped inside, as the drones attack with deadly force. The lesson that the film, Slaughterbots, is trying to impart is clear: tiny killer robots are either here or a small technological advance away. And existing defences are weak or nonexistent.

The ubiquitous nature of chatbots and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator's goal is to convert the feedback into a response that answers the user's previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94% to 75.96% in ranking correct responses on the Personachat dataset, a large improvement given that the original model is already trained on 131k samples.

Alexander Tong GRD '23, a computer science graduate student, and Smita Krishnaswamy, professor of genetics and computer science, won the award for best paper at the annual 2020 Machine Learning for Signal Processing conference, hosted by the Institute of Electrical and Electronics Engineers. From Sept. 21 to Sept. 24, the MLSP conference was hosted virtually at Aalto University in Espoo, Finland. Tong and Krishnaswamy's paper, "Fixing bias in reconstruction-based anomaly detection with Lipschitz discriminators," won the best student paper award alongside two other teams. The paper identified problems present in many machine learning based outlier detection models. The researchers found some cases where these systems do not work -- and this occurs quite often for data types found in bigger data sets.

How would you describe what Artificial Intelligence is? Michael: When we talk about AI, some people tend to imagine the Terminator. For others, it might be Baymax from "Big Hero 6", "WALL-E", or Hal from "2001: A Space Odyssey". What those examples all have in common is the assumption that AI is unavoidably destined (sooner or later) to develop it's own consciousness and autonomous, evil intent. But when it comes to these portrayals of AI, too often they generate an array of fears by focusing our attention on distant and somewhat dystopian possibilities, rather than the present day realities. They usually depict AI as an alignment of computer intelligence with consciousness, but then frighten us by portraying a world where it's not only conscious, but also evil minded and self-motivated to overtake and destroy us. However, I think it's better to talk about AI in a little bit of a different way.

A few weeks ago, I was interviewed by Tim Hughes, of DLA Ignite, about the five biggest mistakes that people make about AI and its impact on the workplace. This article is based on the full interview, which you can find here. It is the use of machinery to replicate a unique human activity, from punch cards that operated sophisticated weaving looms during the industrial revolution, to mid-twentieth century business computers that calculated bills and operated the payroll. Very often these activities are repetitive, error-prone, and in some cases life-threatening. The principle of automation is nearly always the same.

The crystal ball that can predict when, where, and how a customer will spend their money has a new name and it goes by Programmai. This predictive marketing software uses customer data to accurately predict customer spending for both the short and long term. Programmai can even predict the future lifetime value of a customer based on the first interaction. Applying machine learning, marketers can use predictive values from Programmani to develop marketing programs and hone acquisition costs, no longer taking stabs in the dark but instead basing these decisions on justifiable data and forecasts. London TechWatch caught up with CEO Dean Murr to find out how his previous experience at Asos led him to create Programmai, the company's experience raising during the pandemic, and recent funding round.

We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions of each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS, i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to not only learn to generate captions but also predict the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. We additionally extend analysis to an ablation study, applications on holistic image captioning, scene graph generation, and retrieval tasks.

This particular article needs more visibility on Medium, especially to Machiner Learning practitioners. Wael transcribes an interview with Michael Kanaan, an individual that held an AI leadership role in the US Airforce, and is currently working with MIT's primer AI Lab. Early on in the interview, Michael quickly discards the stereotypical portrayal of AI within Hollywood movies and provides the reader with a more accurate description of AI. Michael accurately points out that what we call AI are simply machine learning algorithms that can derive patterns from data, which in turn creates a predictive model of subject of interest, such as a person's behaviour, stock prices etc. The interview goes on to include discussions around the type of individuals that are suitable for roles in AI, a conversation in which Michael debunks the myth that AI-based positions are reserved for individuals with a STEM(Science, technology, engineering, and mathematics) background.

Here's a topic that entails intense controversy, oftentimes sparking loud arguments and heated responses. Do you think that men are better drivers than women, or do you believe that women are better drivers than men? Seems like most of us have an opinion on the matter, one way or another. Stereotypically, men are often characterized as fierce drivers that have a take-no-prisoners attitude, while women supposedly are more forgiving and civil in their driving actions. Depending on how extreme you want to take these tropes, some would say that women shouldn't be allowed on our roadways due to their timidity, while the same could be said that men should not be at the wheel due to their crazed pedal-to-the-metal predilection.