As the world implements artificial intelligence(AI) for data analysis, deep learning techniques play a crucial role and are regarded as one of the intricate aspects. Classifying the proper data attributes and features that matter to various AI systems is important and no one AI tool can solve all problems. AI algorithms must be developed based on specific scenarios. Even then, things can change drastically over time, and AI systems must adjust to those changes.
Before developing any AI tool, the company tasked with the implementation of AI processes must have a thorough understanding of what a business/organization is trying to achieve. Based on that, the AI experts must start labeling the data accurately, which forms the backbone of the AI. The next phase involves calibration and quality reviews to ensure that the business agrees with the approach being taken. During this phase, a lot of guideline refinements can happen. Once there is a clear-cut guideline, solution consulting and solution scoping can start!
Examples Of Problem Solving Techniques In AI
Artificial Intelligence is great owing to its efficient methods of solving. Let’s have a look at two of the problem solving techniques in the realm of AI.
The heuristic method aids in the understanding of a problem and the development of a solution by being solely dependent on the experiments, trial and error techniques, etc.
However, the Heuristic method does not always deliver the best solution to a particular scenario. Instead, it provides effective solutions for attaining near-immediate objectives. Where the traditional approaches fail to deliver a clear-cut solution, the Heuristic method comes to the rescue. They are deployed with optimization algorithms to improve time efficiency because they only provide time-efficient solutions at the expense of accuracy.
2. Searching Algorithms
In AI, searching is a crucial pillar of the approach to solve a problem. The algorithms are utilized by analytical agents or problem-solving mechanisms to locate well-suited solutions.
Furthermore, based on the quality of the solution they deliver, the searching algorithms come with features such as optimality, completeness, time & space complexity, and much more.
Types Of Searching Algorithms
There are two main types of searching algorithms:
- Informed Search, and
- Uninformed Search.
- Informed Search
As a roadmap for effective solutions, these algorithms apply fundamental domain knowledge and interpret available information about a specific situation. The ultimate goal is to produce better and more efficient solutions than an uninformed search algorithm.
- Uninformed Search
These are search algorithms without any domain knowledge. It possesses instructions for traversing all possible results and identifying the possible target solutions. Also known as blind search, uninformed search algorithms do not have specific details about the initial scenario and blindly test for targets while traveling. Breadth-first search, uniform cost search, iterative deepening depth-first search, depth-first search, are all examples of uninformed search algorithms
As evident from here, no one search algorithm can deliver optimal results in every scenario.
The Challenge With Defining Problems & Setting AI Goals
It’s always challenging to turn an AI concept into tangible benefits. It takes proper goals, leadership, skills, and methodology. At the consumer level, it also necessitates buy-in and alignment.
Identifying, setting up goals for AI, and prioritizing must be done by a multi-faceted team. It should include business professionals, domain experts, AI practitioners as well as researchers. This ensures that the company’s goals are met while also incorporating the relevant business knowledge. From governance to compliance to ethics to cost and risk, all are important for an AI venture.
Furthermore, while the technical elements of AI are relatively complex, the outputs are rather simple. In a majority of cases, AI procedures are designed to map a cluster of inputs to one/multiple outputs, each of which falls under a narrow range of possibilities.
Numbers (continuous/discrete), categories and classifications (for instance, spam or not-spam), groups/segments, probabilities, or a sequence (such as words, characters, etc) are all examples of outputs from trained models of AI.
As a result, AI techniques do not automatically solve real-world problems.
They don’t yield income or assist growth on their own, nor do they maximize ROI or boost user engagement and loyalty. Similarly, AI does not intrinsically improve supply chains, operate automobiles, augment human intelligence, detect diseases, or customize advertising.
For instance, setting up a company-wide objective to minimize customer churn by 25% is admirable. Nevertheless, it might not be feasible for most AI applications. As a result, reducing customer churn isn’t a natural outcome of AI solutions. The misalignment between goals like lowering customer churn and actual AI outputs must be addressed and mapped effectively for better results.
In terms of capabilities and implementation, Artificial Intelligence has created inroads in our lives. However, off-the-shelf AI solutions often fail to solve intricate problems of different businesses. The key to AI success is putting together a multi-functional team that determines tangible goals and then allowing these goals to guide AI initiatives and projects. If you are looking to learn more about AI tools and how it can help your business/organization, our experts are always ready to be your guide.