The Latest Trends in Artificial Intelligence and Machine Learning: What Data Scientists Should Know

Data is one of the most powerful weapons in the arsenal of any business nowadays. Businesses use various kinds of data and compile them on certain criteria to best suit their needs. A data scientist thus plays an important role in taking businesses to new heights with their out-of-the-box approach of extracting huge volumes of data into meaningful forms. 

Stages in Data Science

Following are the stages in Data Science:
Acquisition: The first and foremost task in any data science operation is data acquisition. This process involves gathering raw and unstructured data from various sources. A data scientist works on this raw data to form meaningful conclusions.
Maintaining: This raw data gathered from various sources is then collected and supported by the data scientists. Further, this raw data is kept in usable forms. This process could be referred to as data warehousing, cleansing, or staging. 
Processing: The raw data that was kept and collected earlier is now treated with certain pre-defined criteria wherein the data scientist tries to read patterns, recognize trends, and view data ranges so that meaningful conclusions can be fetched from them. Data scientists use tools like data mining, data clustering, data modeling, and summarization in this process.
Analyzing: This forms the most important part of the entire process. Various analyses are drawn from the processed data. Tools like predictive analysis, regression, text mining, qualitative analysis, etc., are used to form conclusions.
Communication: After the data is processed and appropriate analysis is drawn from that data, the results are then communicated to the concerned parties by data scientists, and business decisions are formed based on these results.

What is Artificial Intelligence?

If you aren’t living under a rock, then you must have heard about artificial intelligence. As the name suggests, artificial intelligence is the intelligence of machines rather than living beings. To summarize the previous statement, artificial intelligence uses machines like computers to simulate human intelligence. A Data scientist course and artificial intelligence go hand in hand. AI has the potential to change how humans function and live radically. Various tools of artificial intelligence are used by businesses across the world to automate manual tasks and perform repetitive and detail-oriented functions where humans are more likely to make errors. These AI tools have proved to be more cost-effective, reliable, and efficient than typical human resources.
The Artificial Intelligence focuses primarily on these arenas:
Learning: A typical AI programming is concerned with data acquisition and algorithm preparation that give way to actionable information. Algorithms provide the user with step-by-step instructions on how to carry out specific tasks.
Reasoning: Out of the many algorithms available, in this aspect of Artificial Intelligence, one particular algorithm is chosen to arrive at a desired outcome.
Self-correction: The most striking feature of artificial intelligence is the ability to self-correct. The AI tools can fine-tune algorithms to adapt to the changes and ensure accurate results at all times.
Creativity: AI has advanced leaps and bounds in the field of creativity. Rules-based systems, statistical methods, neural networks, etc, can produce new images, texts, ideas, music, and so on.

The Latest Trends in Artificial Intelligence

AI is progressing manifolds by the day, and a good data scientist must keep tabs on the recent trends in the field of artificial intelligence to stay updated and relevant in the business world. Here are some of the latest trends in the field of AI that every data scientist should know:
Natural Language Processing: This technology eradicates the need to type content manually by using algorithms that capture human language. Many businesses today make use of this technology for language translation, sentiment analysis, text extraction, etc.
Healthcare: Many healthcare centers are looking for ways to reduce the time that is spent on various non-value-adding clerical activities like clinical documentation, clinical data interpretation, speech recognition, and so on by using artificial intelligence and machine learning.
Banking: Banking sectors operate on heavy volumes of data that contain the personal details of thousands of customers. Thus, they use artificial intelligence to help in customer management and data extraction of these customers.
Transportation: Artificial intelligence has taken the transportation industry to a different dimension altogether. Technologies like pedestrian detection, self-driving cars, monitoring road conditions, etc., have changed the entire facet of transportation.
Manufacturing: This sector could use a lot of help from Artificial Intelligence tools for quality control processes, task automation, maintenance and repairs, etc.
Computer Vision: It is one of the many branches of Artificial Intelligence, and it makes use of the visual data inputted by the user to provide desirable results based on the image provided. For example, Google Lens is one such tool that collects images from the user and provides search results relevant to the image provided.
Edge Computing: When source data and the computing process are done in close proximity to facilitate the speedy processing of data in real-time, it is called edge computing. This technology reduces the bandwidth involved in transmission, and thus speed is guaranteed.             
Deep Learning: Another striking branch of artificial intelligence, deep learning uses techniques from machine learning to perform tasks just like humans. It uses networks with multiple layers of processing that are even more accurate than humans. This technology has been widely used in self-driving cars, entertainment platforms, e-commerce, etc.
Explainable AI: Surpassing typical artificial intelligence technology that just provides results, Explainable Artificial Intelligence also provides the reasoning behind these results. It makes the AI systems more transparent and reliable because they explain the logic behind a particular result generated by the system.

The Bottom Line:

Data science engineering is amongst the most sought-after careers in India and abroad because of the dependency on data. A data scientist uses artificial intelligence technology to gather, process, and analyze data and arrive at meaningful conclusions. It is, therefore, important for an aspiring data scientist to be updated regarding the various AI trends so that he can exploit these tools to his advantage.  

Leave a Reply

Your email address will not be published. Required fields are marked *