As cyber-attacks show to be more and more complex and coordinated, cyber-defenders strategy through multi-agent approaches could be key to tackle against cyber-attacks as close as entry points in a networked system. This paper presents a Markovian modeling and implementation through a simulator of fighting cyber-attacker agents and cyber-defender agents deployed on host network nodes. It aims to provide an experimental framework to implement realistically based coordinated cyber-attack scenarios while assessing cyber-defenders dynamic organizations. We abstracted network nodes by sets of properties including agents' ones. Actions applied by agents model how the network reacts depending in a given state and what properties are to change. Collective choice of the actions brings the whole environment closer or farther from respective cyber-attackers and cyber-defenders goals. Using the simulator, we implemented a realistically inspired scenario with several behavior implementation approaches for cyber-defenders and cyber-attackers.

While LLMs have shown impressive capabilities in solving math or coding problems, the ability to make scientific discoveries remains a distinct challenge. This paper proposes a "Turing test for an AI scientist" to assess whether an AI agent can conduct scientific research independently, without relying on human-generated knowledge. Drawing inspiration from the historical development of science, we propose seven benchmark tests that evaluate an AI agent's ability to make groundbreaking discoveries in various scientific domains. These tests include inferring the heliocentric model from celestial observations, discovering the laws of motion in a simulated environment, deriving the differential equation governing vibrating strings, inferring Maxwell's equations from electrodynamics simulations, inventing numerical methods for initial value problems, discovering Huffman coding for data compression, and developing efficient sorting algorithms. To ensure the validity of these tests, the AI agent is provided with interactive libraries or datasets specific to each problem, without access to human knowledge that could potentially contain information about the target discoveries. The ultimate goal is to create an AI scientist capable of making novel and impactful scientific discoveries, surpassing the best human experts in their respective fields. These "Turing tests" serve as intermediate milestones, assessing the AI agent's ability to make discoveries that were groundbreaking in their time. If an AI agent can pass the majority of these seven tests, it would indicate significant progress towards building an AI scientist, paving the way for future advancements in autonomous scientific discovery. This paper aims to establish a benchmark for the capabilities of AI in scientific research and to stimulate further research in this exciting field.

For ASR, this milestone was first claimed in a 2016 research paper by Microsoft (Xiong et al., 2016) reporting that for the first time, they have achieved human parity in word error rate1 (WER) on the Switchboard benchmark (5.8% WER) while also achieving 11% WER on the CallHome benchmark, which is known to be more challenging to transcribe. In addition, the reported decoding speed was only 1.38 real time, which is in the realm of usability for some commercial systems. This announcement was highly publicized even in mainstream media outlets2. A follow-up paper in 2017 claimed further improvement to 5.1% WER on Switchboard but with no report on decoding speed (Xiong et al., 2018). Also in 2017, Google announced a 4.9% WER (on some undisclosed benchmark) at its annual I/O developer conference3.

Ultimate Goal Setting and Achieving is the course for you if you have ever looked at someone else and thought "I want to be like them!" Sometimes when you try to be like that person, you might feel upset or even angry because despite all of your hard work it seems like you are getting nowhere, and everyone else is doing better than you. That's because hard work alone is useless. You need direction and purpose. Think of a car race. It doesn't matter how fast the car is.

Words matter - they help frame our understanding of the world and shape our thoughts, actions and interactions. Inclusion, equity and diversity are all words that are increasingly used but have varying meanings for different issues and groups of people. To compound the issue, they are also often used interchangeably. Treated separately or understood differently, they address only part of our human experience. So the word that resonates with me the most is that of'belonging,' as it focuses on the whole.

In 2013, Peter Ceglinski and Andrew Turton set up their firm, Seabin, with a selfless ambition: "our ultimate goal is pretty simple. It's a world where sea bins are no longer needed for clean up," Ceglinski said, speaking at IBM Think Australia and New Zealand last month. As a report by ZDNet explains, the creators behind Seabin are focusing on building a future where their own product is only used for monitoring the sea, not for cleaning garbage. The cornerstone to this development is artificial intelligence (AI). "What started out as a garbage can has evolved into this global mission focused on data and behavioral change," Ceglinski said at IBM Think.

Learning sophisticated feature interactions is crucial for Click-Through Rate (CTR) prediction in recommender systems. Various deep CTR models follow an Embedding & Feature Interaction paradigm. The majority focus on designing network architectures in Feature Interaction module to better model feature interactions while the Embedding module, serving as a bottleneck between data and Feature Interaction module, has been overlooked. The common methods for numerical feature embedding are Normalization and Discretization. The former shares a single embedding for intra-field features and the latter transforms the features into categorical form through various discretization approaches. However, the first approach surfers from low capacity and the second one limits performance as well because the discretization rule cannot be optimized with the ultimate goal of CTR model. To fill the gap of representing numerical features, in this paper, we propose AutoDis, a framework that discretizes features in numerical fields automatically and is optimized with CTR models in an end-to-end manner. Specifically, we introduce a set of meta-embeddings for each numerical field to model the relationship among the intra-field features and propose an automatic differentiable discretization and aggregation approach to capture the correlations between the numerical features and meta-embeddings. Comprehensive experiments on two public and one industrial datasets are conducted to validate the effectiveness of AutoDis over the SOTA methods.

Clint Coghill co-founded Backstop Solutions Group in 2003 and now leads the Backstop Executive Team as CEO. Thanks to the sheer amount and complexity of data that is generated each day, the office of the chief investment officer has effectively expanded into an intelligence unit in recent years. Data is flowing in from multiple sources, including custodians, fund administrators, consultants and managers, to name just a few, and that data is flowing into inboxes and shared drives in tens of thousands of emails per year. All of that information could live in any number of functional and technology silos within a typical investment firm, while at the same time, complex assets often have life cycles that outlast investment staff, leading to issues around knowledge transfer and data continuity. Machine learning and artificial intelligence (AI) can absolutely be valuable tools in collecting, analyzing, managing and putting that vast amount of information to use.

Two years ago, Berkeley computer science professor and AI expert Michael I. Jordan wrote an article warning against overinflating the claims of AI. He declared the AI revolution something we could only hope to reach in the future. I'd argue the revolution is now here. It just doesn't look the way sci-fi always portrayed it. As Jordan rightly points out in his article, the term "artificial intelligence" or "AI" is applied so widely to so many technologies that it has become practically meaningless.

Artificial intelligence is going to change how humanity thinks about the role of culture, god, faith, reality and ourselves. Can artificial intelligence solve world hunger and bring eternal peace? We will see that when the time comes but the inevitability of artificial intelligence becoming smarter than human has raised many questions about the long-term survival of the human race. Yes, some are myths and some statements made recently are overhyped but there is no doubt if machine's goals are misaligned with ours then we need to ask ourselves What kind of future we want? What is a good life? The truth behind the harmony of this cosmos as professed by science, the will to take over authority over all things, and with "knowledge is power" philosophy, today's man is a greedy man. Today's man is ready to play with the mind of cosmos. He thinks he has the power to be free and with this power, he will be free eternally.