IBM announces new hybrid cloud and AI capabilities
IBM has announced advances in artificial intelligence (AI), hybrid cloud, and quantum computing at the company's Think conference today. These innovations by IBM's are to help in their role with assisting their clients and partners accelerate their digital transformations, return to work smarter, and build strategic ecosystems that can drive better business outcomes.
"In the same way that we electrified factories and machines in the past century, we will use hybrid cloud to infuse AI into software and systems in the 21st century. And one thing is certain: this is a future that must be built on a foundation of deep industry collaboration. No one understands this better than IBM, which is one of the reasons we are boosting investment in our partner ecosystem." said IBM Chairman and CEO Arvind Krishna.
A new IBM study on the adoption of AI for business reveals that the need to embed AI into business processes became more urgent during the pandemic. Of IT professionals surveyed, 43% said that their companies had accelerated their rollout of AI. And nearly half of global IT professionals surveyed said they evaluate AI providers in large part on their ability to automate processes.
New innovations to help businesses
IBM has announced various new innovations to help businesses, and here are a few:
- Mono2Micro to help with cloud migration - IBM has added a new capability into WebSphere Hybrid Edition that enables enterprises to optimise and modernise their applications for hybrid cloud. IBM Mono2Micro uses AI developed by IBM Research to analyse large enterprise applications and provide recommendations on how to best adapt them for the move to the cloud.
- AutoSQL - AutoSQL (Structured Query Language) automates how customers access, integrate and manage data without ever having to move it, regardless of where the data resides or how it is stored. According to IBM AutoSQL solves ‘one of the most critical pain points customers are facing as they look to reduce the complexity of curating data for AI and eliminate the high cost of moving data, while also uncovering hidden insights to make more accurate AI-driven predictions.’ AutoSQL will be one of several new technologies woven into a new data fabric in the Cloud Pak for Data.
- Watson Orchestrate to helps professionals automate work to increase productivity - Watson Orchestrate is a new interactive AI capability designed to increase the personal productivity of business professionals across sales, human resources, operations and more. Requiring no IT skills to use, Watson Orchestrate enables professionals to initiate work in a very human way, using collaboration tools such as Slack and email in natural language. It also connects to popular business applications like Salesforce, SAP and Workday. Watson Orchestrate uses a powerful AI engine that automatically selects and sequences the pre-packaged skills needed to perform a task, and connects with applications, tools, data and history on-the-fly
- Qiskit Runtime software boosts quantum circuit processing speed by 120x - IBM is making it faster and easier for developers to use quantum software by introducing Qiskit Runtime. This software is containerised and hosted in the hybrid cloud, instead of running most of its code on the user's computer. Together with improvements in both the software and processor performance, this allows Qiskit Runtime to boost the speeds of quantum circuits, the building blocks of quantum algorithms, by 120 times
The announcements made during today's Think event come just days after IBM unveiled the world's first two nanometer chip, which will enable faster, more efficient computing.
Facebook Develops AI to Crackdown on Deepfakes
In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale?
Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.”
The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.
Why Reverse Engineering?
Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient.
First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source.
How It Works
Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.
To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters.
What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’.
What Did They Find?
‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising.
Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.