"Search is a problem-solving technique that systematically explores a space of problem states, i.e., successive and alternative stages in the problem-solving process. Examples of problem states might include the different board configurations in a game or intermediate steps in a reasoning process. This space of alternative solutions is then searched to find an answer. Newell and Simon (1976) have argued that this is the essential basis of human problem solving. Indeed, when a chess player examines the effects of different moves or a doctor considers a number of alternative diagnoses, they are searching among alternatives."
– from Section 1.2 of Chapter One of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005).
Amazon.com Inc. has adjusted its product-search system to more prominently feature listings that are more profitable for the company, said people who worked on the project--a move, contested internally, that could favor Amazon's own brands. Late last year, these people said, Amazon optimized the secret algorithm that ranks listings so that instead of showing customers mainly the most-relevant and best-selling listings when they search--as it had for more than a decade--the site also gives a boost to items that are more profitable...
It seems Amazon may be putting profits before customers. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers. Programmers involved with the search algorithm are said to have opposed the change, as Amazon's principles stress they'work to earn and keep customer trust'. The changes were cited by sources familiar with the situation, who claimed Amazon's product-search system was changed last year. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers Prior to the switch, algorithms would first show products that were bestsellers or relevant to what customers were looking to purchase.
Recent technological advancements have brought improvements to the recruitment process. It has changed the sourcing, reviewing, and consideration of job candidates. Now, employers don't need to go through dozens of resumes to find top talent. Sometimes, open posts are not advertised publicly. Therefore, you must know the technologies that are used in recruitment today when you are job seeking.
Ignoring hyperparameters helps reduce the boundary's complexity (see original paper for more info). Since the MAB extracts the \(B\) best out of \(B \cdot p\) candidates in each round, most MABs and their runtimes multiply the \(p 2\) factor more than any other parameter. It thus becomes apparent: the algorithm's efficiency decreases with feature abundant problems. Tabular data is structured data represented by tables, wherein columns embody features and rows instances. For instance, we use the bike rental data to demonstrate the anchors approach's potential to explain ML predictions for selected instances.
Data structures are presented in a container hierarchy that includes stacks and queues as non-traversable dispensers, and lists, sets, and maps as traversable collections. Algorithm analysis is introduced and applied to linear and binary search, bubble sort, selection sort, insertion sort, merge sort and quicksort. The book also covers heaps and heapsort, unbalanced binary search trees, AVL trees, 2-3 trees, hashing, graph representations, and graph algorithms based on depth-and breadth-first search.
AI is now integrated into countless scenarios, from tiny drones to huge cloud platforms. Every hardware platform is ideally paired with a tailored AI model that perfectly meets requirements in terms of performance, efficiency, size, latency, etc. However even a single model architecture type needs tweaking when applied to different hardware, and this requires researchers spend time and money training them independently. Popular solutions today include either designing models specialized for mobile devices or pruning a large network by reducing redundant units, aka model compression. A group of MIT researchers (Han Cai, Chuang Gan and Song Han) have introduced a "Once for All" (OFA) network that achieves the same or better level accuracy as state-of-the-art AutoML methods on ImageNet, with a significant speedup in training time. A major innovation of the OFA network is that researchers don't need to design and train a model for each scenario, rather they can directly search for an optimal subnetwork using the OFA network.
The latest neural network to impress is DeepCubeA from Forest Agostinelli, Stephen McAleer, Alexander Shmakov and Pierre Baldi of the University of California, Irvine. This is a deep neural network that learns a range of combinatorial puzzles - sliding block15, 24, 35, 48 puzzles, Lights Out, Sokoban and, of course, Rubik's cube. The network learns a reinforcment value function, but it does this "backwards". That is, it starts from a solution and randomly takes moves away from the goal. As it steps away from the goal, the moves and configurations become increasingly low in value, - i.e. they are moving away from the goal.
A neural architecture, which is the structure and connectivity of the network, is typically either hand-crafted or searched by optimizing some specific objective criterion (e.g., classification accuracy). Since the space of all neural architectures is huge, search methods are usually heuristic and do not guarantee finding the optimal architecture, with respect to the objective criterion. In addition, these search methods might require a large number of supervised training iterations and use a high amount of computational resources, rendering the solution infeasible for many applications. Moreover, optimizing for a specific criterion might result in a model that is suboptimal for other useful criteria such as model size, representation of uncertainty and robustness to adversarial attacks. Thus, the resulting architectures of most strategies used today, whether hand crafting or heuristic searches, are densely connected networks, which are not an optimal solution for the objective they were created to achieve, let alone other objectives.
Another significant requirement is the need to find an efficient method for reducing the amount of computational searching for a match or a solution. Considerable important work has been done on the problem of pruning a search space without affecting the result of the search. One technique is to compare the value of completing a particular branch versus another. Of course, the measurement of value is a problem. As real-time applications become more important, search methods must become even more efficient in order for an Al system to run in real-time.
"Our AI takes about 20 moves, most of the time solving it in the minimum number of steps," Baldi says. "Right there, you can see the strategy is different, so my best guess is that the AI's form of reasoning is completely different from a human's." The ultimate goal of projects such as this one is to build the next generation of AI systems, Baldi says. Whether they know it or not, artificial intelligence touches people every day through apps such as Siri and Alexa and recommendation engines working behind the scenes of their favorite online services. "But these systems are not really intelligent; they're brittle, and you can easily break or fool them," Baldi says.