The multidimensional knapsack problem is a well-known constrained optimization problem with many real-world engineering applications. In order to solve this NPhard problem, a new modified Imperialist Competitive Algorithm with Constrained Assimilation (ICAwICA) is presented. The proposed algorithm introduces the concept of colony independence - a free will to choose between classical ICA assimilation to empire's imperialist or any other imperialist in the population. Furthermore, a constrained assimilation process has been implemented that combines classical ICA assimilation and revolution operators, while maintaining population diversity. This work investigates the performance of the proposed algorithm across 101 Multidimensional Knapsack Problem (MKP) benchmark instances. Experimental results show that the algorithm is able to obtain an optimal solution in all small instances and presents very competitive results for large MKP instances.

Looking back at the history of calculators, one can see that they become less functional and more computationally expensive over time. A modern calculator runs on a personal computer and is drawn at 60 fps only to help us click a few digits with a mouse pointer. A search engine is often used as a calculator, which means that nowadays we need the Internet just to add two numbers. In this paper, we propose to go further and train a convolutional neural network that takes an image of a simple mathematical expression and generates an image of an answer. This neural calculator works only with pairs of double-digit numbers and supports only addition and subtraction. Also, sometimes it makes mistakes. We promise that the proposed calculator is a small step for man, but one giant leap for mankind.

Increasingly growing Cryptocurrency markets have become a hive for scammers to run pump and dump schemes which is considered as an anomalous activity in exchange markets. Anomaly detection in time series is challenging since existing methods are not sufficient to detect the anomalies in all contexts. In this paper, we propose a novel hybrid pump and dump detection method based on distance and density metrics. First, we propose a novel automatic thresh-old setting method for distance-based anomaly detection. Second, we propose a novel metric called density score for density-based anomaly detection. Finally, we exploit the combination of density and distance metrics successfully as a hybrid approach. Our experiments show that, the proposed hybrid approach is reliable to detect the majority of alleged P & D activities in top ranked exchange pairs by outperforming both density-based and distance-based methods.

We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.

A rare fully-functional Apple-1 Computer has sold for $458,711 when it went under the hammer at an auction in Boston this week. The Apple-1 was the first product to be developed under the Apple name by company co-founders Steve Jobs and Steve Wozniak and launched in 1976. Alongside the pioneering product, the auction also saw the sale of the lifetime collection of Apple product design engineer Jerry Manock. The collection features a Macintosh PowerBook signed by Steve Jobs which sold for $12,671 (£10,284) and a neon Apple logo that went for $1,915 (£1,554). Meanwhile, a'think different' watch from the brand's famous ad campaign sold for $1,375 (£1,116).

This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related.

Welcome back to my recommended reading list, with pieces this week on the latest on coronavirus, how Facebook is using AI to tackle fake accounts and the carbon footprint of your online habits. This week's photo, by Reuters photographer Rodi Said, shows a boy waiting with his mother as they queue with others for humanitarian and medical help after leaving Baghouz, the last stronghold of the Islamic State caliphate, in Deir Al Zor, Syria, on March 5, 2019. The image was selected as the photo the year in a vote by Thomson Reuters staff around the world. Read on for this week's picks... Twenty-one people aboard a cruise ship that was barred from docking in San Francisco have tested positive for coronavirus, U.S. officials said on Friday, adding to the more than 100,000 cases of the fast-spreading illness across the world. The outbreak has killed more than 3,400 people and spread across more than 90 nations, with seven countries reporting their first cases on Friday.

May 12 - 13, 2020 - Mayo Civic Center - Rochester, Minnesota Mayo Clinic, driven by its values, adopts a future-forward approach to leading health care transformation. Leveraging medical excellence and digital health sciences, artificial intelligence plays a critical role. The Mayo Clinic Artificial Intelligence Symposium aims to bring the health care AI community together to learn about current activities, share best practices, and foster collaborations toward digital health and medicine. We are currently accepting abstracts for the Artificial Intelligence Symposium 2020. Proposals will be reviewed and confirmed on a first come, first serve basis.

How many times have you called customer service expecting a quick answer, but were instead passed around to a number of different people who didn't have your full story, forcing you to repeat yourself unnecessarily? And how many times did this lead to a much longer-than-expected resolution? We've all experienced this at least once and as a result, we understand the importance of streamlining experiences to keep customers happy. It begs the question: How can you deliver an experience that doesn't leave customers running for the hills? By incorporating artificial intelligence into claims, inquiries, and other policy service processes, insurers can deliver more accurate information and streamline customer experiences.