Ten years ago, if you bandied around the word Artificial Intelligence in a Kenyan newsroom, you would be branded an illusionist, today it is the buzzword. AI, machine learning and data processing are fast becoming essential in any newsroom that hopes to survive the digital revolution avalanche. In fact, job titles such as data scientist, data analyst, content strategist, content engineer and impact editors are fast gaining currency in media houses deemed digital journalism early adopters. Today, it is almost impossible to distinguish between an article authored by a machine and that written by a journalist, AI has enabled editors to figure out their audiences needs to granular levels and offer them articles that "speak to their souls". If the trend of automation of journalism catches on in the continent fast enough, in a few years it would be difficult to tell if the news anchor calling you "mpendwa msikilizaji" is a robot or human.

As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).

Simon did an introductory talk about deep learning techniques applied for natural language processing: CNNs, LSTMs, bi-LSTMs, CRF bi-LSTMs are networks often used to label sentences at the word, or even the character level. Tan did a visual introduction to Topological Data Analysis (TDA), the application of discrete topology to study point clouds. These techniques allow for a robust description of the point clouds properties at multiple scales via persistence diagrams. Robustness and persistence of patterns at multiple scales are a desirable properties, especially in the case of noisy and highly stochastic financial time series. Tan uses the persistence diagrams as features to a machine learning classifier (say XGBoost) to predict ETFs returns.

Artificial intelligence is getting better and better at generating an image in response to a handful of words, with publicly available AI image generators such as DALL-E 2 and Stable Diffusion. Now, Meta researchers are taking AI a step further: they're using it to concoct videos from a text prompt. Meta CEO Mark Zuckerberg posted on Facebook on Thursday about the research, called Make-A-Video, with a 20-second clip that compiled several text prompts that Meta researchers used and the resulting (very short) videos. The prompts include "A teddy bear painting a self portrait," "A spaceship landing on Mars," "A baby sloth with a knitted hat trying to figure out a laptop," and "A robot surfing a wave in the ocean." The videos for each prompt are just a few seconds long, and they generally show what the prompt suggests (with the exception of the baby sloth, which doesn't look much like the actual creature), in a fairly low-resolution and somewhat jerky style.

This year's summer holidays provided me with a stark realisation: how much AI has become an integral part of everyday life. I spent a happy two weeks driving the south of Italy with my family. It was very enjoyable trip and provided a welcome rest. I cannot help but also reflect on the difference in the experience of driving abroad now and from before. Ten years ago, our holiday started in a car full of little people failing to follow the printed route map from the airport towards our Eurocamp site.

Set in undetermined future society where robotics are a major part of everyday life, from performing household chores to construction and more, Runaway follows Officer Jack Ramsay (Tom Selleck), head of the so-called'Runaway Squad'. His hi-tech unit deals with out of control robots, intervening where humans may be endangered by the machines' malfunctions. When Ramsay discovers a plot by criminal genius Luther (Gene Simmons) to sell advanced microchips and cutting edge weaponry to the highest bidder, he finds himself taking on not only Luther, but also the dangerous killer's deadly robotic creations. Written and directed by the late Michael Crichton, Runaway continues the theme of the dangers of technology affecting the lives of humans that had been previously visited in his previous works Westworld and Looker. Reviewing Runaway now, thirty years after it's original release, is a real eye opener to Crichton's foresight.

AI will eventually produce a complete narrative movie from beginning to end. Look at "The Crow," a fantastic AI-produced short film made by Glenn Marshall with the aid of OpenAI's CLIP. This neural network effectively picks up visual concepts from natural language supervision. The movie is about a dancer who transforms into a crow, and it takes place in a lonely, post-apocalyptic-looking world created by artificial intelligence. The short live-action film Painted's video frames were fed into CLIP, where they were later used to create a video of "a painting of a crow in a desolate landscape," according to the artist.

Within the text analysis and processing fields, generated text attacks have been made easier to create than ever before. To combat these attacks open sourcing models and datasets have become a major trend to create automated detection algorithms in defense of authenticity. For this purpose, synthetic text detection has become an increasingly viable topic of research. This review is written for the purpose of creating a snapshot of the state of current literature and easing the barrier to entry for future authors. Towards that goal, we identified few research trends and challenges in this field.

A machine learning algorithm that can flag papers that may have come from paper mills could help publishers fight fake scientific studies. Paper mills may be the biggest organised fraud perpetrated on scientific journals ever. While there have been instances of individual researchers manipulating images or simply inventing data, paper mills serve up professional fakery on an industrial scale. Buyers can purchase a paper, or authorship of one, on any topic based on entirely made-up results. Biochemical and biomedical journals have been hit particularly hard, flooded with hundreds of fake research manuscripts.

How can something understood as the universal symbol for joy so easily become the makings of our worst nightmares? Unhappy, unsettling smiles--like those in Todd Phillips' Joker or the truly chilling masks donned by Ethan Hawke in last year's The Black Phone--appear here to stay as fixtures of the horror genre. Paramount's new flick, directed by Parker Finn, makes the concept its very premise, with the movie following a psychiatrist plagued by smiles everywhere she turns. Baseball fans got a taste of her strife thanks to a stunt marketing campaign for Smile featuring actors smiling creepily behind the dugout. Whereas the 2018 horror film Truth or Dare used CGI to stretch the smiles of the actors like a Snapchat filter befitting the uncanny valley, the smiles in Smile for the most part do not appear digitally modified, by my estimate.