Back in 2019, I wrote about the "container tax." In simple terms, this is the additional cost to use containers properly within a cloud-based application. It includes development, operations, and other expenses that containers incur. The goal of leveraging containers is to offset the additional costs with the benefits they offer. Many other technologies come with additional costs, which may or may not justify using that specific technology.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' Critics who slammed OpenAI's ChatGPT system as "woke" and riddled with liberal bias are creating their own chatbots. Tesla and Twitter CEO Elon Musk is reportedly assembling a team of artificial intelligence experts to build an alternative to ChatGPT – and other similar ideas may also be in development. The New York Times reported that the founder of Gab, a right-wing social media platform, is working on an AI software with "the ability to generate content freely without the constraints of liberal propaganda wrapped tightly around its code." OpenAI's GPT-4 is the latest deep learning model from the company that "exhibits human-level performance on various professional and academic benchmarks," according to the lab.

As the world contends with the lingering catastrophic effects of a global pandemic, set against the ongoing backdrop of climate disasters and worsening conflict and humanitarian disasters, the need for rapid scientific advancement in response to crisis has never been so apparent. We can't say we weren't warned. In 2015, the United Nations released The 2030 Agenda for Sustainable Development, a shared blueprint constituting "an urgent call for action by all countries -- developed and developing -- in a global partnership." Central to the agenda are the 17 Sustainable Development Goals (SDGs) that address a range of urgent needs for humanity, from poverty alleviation and gender equality, to decent work, sustainable cities and communities, a clean environment, affordable and clean energy, and peace. The goals were founded on decades of input from global researchers, stakeholders and policy makers.

Microsoft is introducing new chat applications that make use of artificial intelligence (AI) to help cybersecurity teams thwart assaults and avert intrusions. They are a part of the most recent wave of AI software releases from Microsoft and go by the name Copilots. The most recent version of Copilot uses data specific to security to find links between hacking components more quickly, as well as OpenAI's cutting-edge GPT-4 language system. It can, for instance, be used to find connections between a questionable email, a malicious software file, or the vulnerable areas of a system. The Security Copilot depends on data from governmental agencies and Microsoft experts who keep an eye on nation-states and cybercriminal organisations.

Kissinger, former Secretary of State in the Nixon & Ford administrations; Schmidt, former CEO of Google; Huttenlocher, an acclaimed computer vision research who is currently a dean at MIT's Schwarzman College of Computing. Why it's good: The book's trio of authors brings their expertise in statecraft, business, and academia to explore how AI is set to reshape society. "AI's promise of epoch-making transformations--in society, economics, politics, and foreign policy--portends effects beyond the scope of any single author's or field's traditional focuses," the authors contend in an online preview of the book. In the time since The Age of A.I. was published, a revolution in generative AI has brought us much closer to the promise of artificial general intelligence -- the representation of human cognitive abilities in software-- making the book's discussion of how society will change as machines increasingly perform human tasks all the more relevant.

In case you have been somewhere else in the solar system, here is a brief AI news update. My apologies if it sounds like the opening paragraph of a bad science fiction novel. On 14 March 2023, OpenAI, a company based in San Francisco and part owned by Microsoft, released an AI system called GPT-4. On 22 March, a report by a distinguished group of researchers at Microsoft, including two members of the US National Academies, claimed that GPT-4 exhibits "sparks of artificial general intelligence". On 29 March, the Future of Life Institute, a non-profit headed by the MIT physics professor Max Tegmark, released an open letter asking for a pause on "giant AI experiments". It has been signed by well-known figures such as Tesla's CEO, Elon Musk, Apple's co-founder Steve Wozniak, and the Turing award-winner Yoshua Bengio, as well as hundreds of prominent AI researchers.

The International Federation of Robotics predicted in 2018, 1.3 million industrial robots will be in operation. Robotics is still growing as a significant industry. The following are the top 5 industries utilizing robotics to boost efficiency and convenience for both businesses and customers. Several aspects of health care, including surgery, therapy, rehabilitation, patient companionship, and daily activities, could be altered by advances in robotics. Robotic medical devices aren't meant to replace human healthcare workers; rather, they're meant to make their jobs simpler.

This paper aims to detect rice field damage from natural disasters in Bangladesh using high-resolution satellite imagery. The authors developed ground truth data for rice field damage from the field level. At first, NDVI differences before and after the disaster are calculated to identify possible crop loss. The areas equal to and above the 0.33 threshold are marked as crop loss areas as significant changes are observed. The authors also verified crop loss areas by collecting data from local farmers. Later, different bands of satellite data (Red, Green, Blue) and (False Color Infrared) are useful to detect crop loss area. We used the NDVI different images as ground truth to train the DeepLabV3plus model. With RGB, we got IoU 0.41 and with FCI, we got IoU 0.51. As FCI uses NIR, Red, Blue bands and NDVI is normalized difference between NIR and Red bands, so greater FCI's IoU score than RGB is expected. But RGB does not perform very badly here. So, where other bands are not available, RGB can use to understand crop loss areas to some extent. The ground truth developed in this paper can be used for segmentation models with very high resolution RGB only images such as Bing, Google etc.

We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the weighted minimum mean squared error (WMMSE) method, a classical approach to solving this problem, and the principle of algorithm unfolding, we present unfolded WMMSE (UWMMSE) for MU-MIMO. This method learns a parameterized functional transformation of key WMMSE parameters using graph neural networks (GNNs), where the channel and interference components of a wireless network constitute the underlying graph. These GNNs are trained through gradient descent on a network utility metric using multiple instances of the beamforming problem. Comprehensive experimental analyses illustrate the superiority of UWMMSE over the classical WMMSE and state-of-the-art learning-based methods in terms of performance, generalizability, and robustness.

Training-time defenses, known as adversarial training, incur high training costs and do not generalize to unseen attacks. Test-time defenses solve these issues but most existing test-time defenses require adapting the model weights, therefore they do not work on frozen models and complicate model memory management. The only test-time defense that does not adapt model weights aims to adapt the input with self-supervision tasks. However, we empirically found these self-supervision tasks are not sensitive enough to detect adversarial attacks accurately. In this paper, we propose DRAM, a novel defense method to detect and repair adversarial samples at test time via Masked autoencoder (MAE). We demonstrate how to use MAE losses to build a Kolmogorov-Smirnov test to detect adversarial samples. Moreover, we use the MAE losses to calculate input reversal vectors that repair adversarial samples resulting from previously unseen attacks. Results on large-scale ImageNet dataset show that, compared to all detection baselines evaluated, DRAM achieves the best detection rate (82% on average) on all eight adversarial attacks evaluated. For attack repair, DRAM improves the robust accuracy by 6% ~ 41% for standard ResNet50 and 3% ~ 8% for robust ResNet50 compared with the baselines that use contrastive learning and rotation prediction.