Foundation models have had a big impact in recent years and billions of dollars are being invested in them in the current AI boom. The more popular ones, such as Chat-GPT, are trained on large amounts of Internet data. However, it is becoming apparent that this data is likely to be exhausted soon, and technology companies are looking for new sources of data to train the next generation of foundation models. Reinforcement learning, RAG, prompt engineering and cognitive modelling are often used to fine-tune and augment the behaviour of foundation models. These techniques have been used to replicate people, such as Caryn Marjorie. These chatbots are not based on people's actual emotional and physiological responses to their environment, so they are, at best, a surface-level approximation to the characters they are imitating. To address these issues, we have developed a recording rig that captures what the wearer is seeing and hearing as well as their skin conductance (GSR), facial expression and brain state (14 channel EEG). AI algorithms are used to process this data into a rich picture of the environment and internal states of the subject. Foundation models trained on this data could replicate human behaviour much more accurately than the personality models that have been developed so far. This type of model has many potential applications, including recommendation, personal assistance, GAN systems, dating and recruitment. This paper gives some background to this work and describes the recording rig and preliminary tests of its functionality. It then suggests how a new type of foundation model could be created from the data captured by the rig and outlines some applications. Data gathering and model training are expensive, so we are currently working on the launch of a start-up that could raise funds for the next stage of the project.

In this paper we investigate the vulnerability that facial recognition systems present to adversarial examples by introducing a new methodology from the attacker perspective. The technique is based on the use of the autoencoder latent space, organized with principal component analysis. We intend to analyze the potential to craft adversarial examples suitable for both dodging and impersonation attacks, against state-of-the-art systems. Our initial hypothesis, which was not strongly favoured by the results, stated that it would be possible to separate between the "identity" and "facial expression" features to produce high-quality examples. Despite the findings not supporting it, the results sparked insights into adversarial examples generation and opened new research avenues in the area.

Inc (AMZN.O) is planning to roll out warning cards for software sold by its cloud-computing division, in light of ongoing concern that artificially intelligent systems can discriminate against different groups, the company told Reuters. Akin to lengthy nutrition labels, Amazon's so-called AI Service Cards will be public so its business customers can see the limitations of certain cloud services, such as facial recognition and audio transcription. The goal would be to prevent mistaken use of its technology, explain how its systems work and manage privacy, Amazon said. The company is not the first to publish such warnings. International Business Machines Corp (IBM.N), a smaller player in the cloud, did so years ago.

Question: How does it fare against caricature artwork? Thought the service/model concluded that they are not the same person, the above two images returned a similarity score of 70.6%. Question: How does it fare against edited pictures? At my side of the world, the word "Meitu" is a name of a mobile app from China that has transcended into being a verb for "picture enhancement" in our pop culture. The above is a screengrab from the famous South China Morning Post's video on Youtube on Chinese live streamers.

I'm going to discuss and go through a full application that was built to explore: Serverless - Serverless is clearly still running on a server, but put simply it's using resources on demand, with AWS taking care of the infrastructure and servers. Event Driven Architecture - Going hand in hand with serverless is being an event driven architecture - because we only pay for what we use, having an application that has absolutely nothing running until it has to reactively process a message. We also will also see how separate components, or Microservices, can be separated by the Event Bus and could theoretically be developed by whole separate teams and Code Bases. Cloud Native Patterns - I've tried to include lot's of different use cases to show different patterns that can be used when building Cloud Native applications - from analytics, orchestration, etc The vehicle for this journey will be a Twitter Bot; an application that can be fully reactive but something that isn't bound by specific domain behaviours, and not complex to understand. The important part that you need to know is that Twitter has an API called the Account Activity API which can be configured to fire webhooks when any activity happens with a particular account. This means we will be sent events when receiving a mention for example - which is an ideal way to explore these technologies that has an internal and external domain. As everything should be built in my opinion, the infrastructure is specified with code, so the whole application from the actual code, to the setting up of infrastructure is from a single application built using the AWS Cloud Development Kit.

Plants that have a lack or deficiency of nitrogen have performance problems and show chlorosis or yellow or light green coloration. They are more prone to pests and diseases. The lack of potassium in the soil also causes the leaves to appear yellowish or blue-green, with dark yellow edges. They are more susceptible to fungal attack. With lack of water, dry soil, the leaves are decayed and also show a yellow hue.

Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. Furthermore, histological assessment is a time-consuming process that requires highly trained professionals. With significant technological advances in the last decade, techniques such as whole slide imaging (WSI) and deep learning (DL) are now widely available.

Facebook is almost fully abandoning facial recognition, but its parent company Meta isn't. On November 2, the world's largest social media network said it's going to stop using facial recognition technology (FRT) systems on its platform and delete facial recognition templates for billions of people. However, Meta spokesperson Jason Grosse told Recode that the move doesn't apply to its upcoming metaverse products. The social media firm rebranded to Meta on October 29 when chief executive Mark Zuckerberg announced that the company is shifting its focus to building a future metaverse. "The next platform will be even more immersive -- an embodied internet where you're in the experience, not just looking at it. We call this the metaverse, and it will touch every product we build," Zuckerberg said in a letter following the Facebook Connect event.

Ever wanted to use a technology that can identify specific objects contained in any given image? Well look no further as AWS Rekognition fulfils that need. AWS Rekognition is a tool that is part of the AWS Cloud Infrastructure ecosystem. It primarily serves to make it easy to add image and video analysis to any application using machine learning that requires zero knowledge of artificial intelligence. AWS Rekognition can be used to recognizing objects, people, text and many more. One notable use case for Rekognition is to use it to identify dog breeds via a dog image.

In 2017, Amazon officially announced three new features to its "Amazon Rekognition" software package. The software was launched the previous year with the promise to dramatically increase developers' use of machine learning in the analysis of digital images. The new features included "detection and recognition of text in images, real-time face recognition across tens of millions of faces, and detection of up to 100 faces in challenging crowded photos," per the press release. The breadth of the new features demonstrated how far machine learning had come in just a few years, and the release merely touched the surface of the widespread applicability of Amazon's ever-improving software. Describing the technology's immediate impact in the battle to end human trafficking, as well as its use by social media sites such as Pinterest as a way of extracting "rich text" and thereby improving the cataloging of users' images, Amazon seemed keen to telegraph how widely Rekognition was already used.