Contact Us
×

Category: Management

With rampant use of artificial intelligence and machine learning, how are financial institutions dealiing with problems related to data bias and transparency?

Posted on by Imarticus Learning

The public and private sectors are increasingly turning to machine learning (ML) algorithms and artificial intelligence (AI) systems to automate every decision-making process, and financial institutions are no exception.

In addition to widespread use in the capital markets, artificial intelligence and machine learning are used in financial services to make insurance decisions, monitor user behavior, recruitments, fraud detection, credit referencing, and underwriting loans.

However, while AI and ML have brought innumerable benefits to financial institutions, they also have their share of woes in the form of data biases and transparency issues. The question is, how are financial institutions dealing with these problems?

Bias and Transparency in the AI Context

AI systems are powered by algorithms that “train” by reviewing massive datasets to ultimately identify patterns and make decisions based on the observations. Hence, these systems are no better than the fed data, resulting in unconscious data biases.

On the contrary, transparency in the context of AI refers to the ability to explain AI-based decisions. Given the increasingly complex findings and algorithms, ensuring transparency to different stakeholders is vital in the financial sector, both from compliance and business value perspectives.

Biases can occur in many ways. For example, bias due to incomplete data occurs when the AI system has been trained on data that is not representative of the population.

Likewise, the dataset could be biased towards previous decision-making processes, the programmer may introduce their own bias into codes, or business policies pertaining to AI decisions could be biased themselves. The bias of any form eventually leads to unfairness and inequities in financial services.

Dealing With AI Bias and Transparency

Although the use of AI and ML give rise to data bias and transparency issues, they have become indispensable for the functioning of financial services. So, the only course of action left to financial institutions is to adopt ways to get around the problems. Some of them are listed below:

  • Financial institutions and firms can have appropriate controls and monitoring tools to ensure that new data entering the pool is reliable and of high quality. 
  • In addition, some organizations have developed tools to determine if a potential AI solution is biased. 
  • When building AI systems, it is wise to gather a team with domain expertise, model development skills, data engineering capabilities, and commercial expertise. 
  • Organizations can undertake impact assessments of the AI solutions to ensure they are transparent and explainable, as well as determine how the AI-based decision-making process will impact customers. 
  • When engaging with AI technologies, financial services can apply safeguards to ensure that business outcomes are achieved, and customers’ interests are protected. 
  • Another way to minimize data biases is to be open on the user data, match and align data with the target segment, and set up review cycles with legal and statistical experts. 
  • Tracking mechanisms that allow one to track the decision-making mechanism of algorithms can be put in place to eliminate bias and ensure transparency as much as possible. 
  • Lastly, it is pertinent for institutions to document their approach to handling bias and review it after every stage of development and use of the algorithm.

What to Look for in an Artificial Intelligence Course?

If you want to learn AI and ML, there are several online courses you can choose from. An AI and ML certification course that makes you future-ready will have a robust curriculum covering critical concepts related to data science, machine learning, NLP, deep learning, and computer vision.

In addition, the program should offer in-depth experiential learning through hands-on involvement with real-world projects.

Tools Data Scientists Use to Make Precise Predictions

Posted on by Imarticus Learning

It is no secret that the accuracy of predictions in the business world can make or break a company. Data scientists create these accurate predictions to help businesses understand what will happen and prepare for it. It’s not easy, but data science has many tools that can make this process easier. In this blog post, we’ll explore some of those tools and how they work!

Tools data scientists use to make precise predictions:

Predictive analytics algorithms help data scientists predict future events and behaviors by using existing data. These tools build mathematical models that capture the connection between demographics, location, time of day, etc., and measurements such as the number of web visits or revenue.

One type of algorithm is a decision tree, a set of rules used to classify things. For example, if the weather is sunny and warm, there’s an 80 percent chance it will be hot outside. Still, if the weather is rainy or cool, there’s only a 30 percent chance it will be hot outside. A data scientist can use this information to determine an appropriate temperature for an office during a particular weather pattern.

Another type of algorithm is a random forest based on the same idea as decision trees but performs better in some cases. Random forests use when data scientists want to make accurate predictions with many different variables. The randomized process behind the tool helps ensure that each prediction is different from the last one.

Artificial neural networks (ANNs) are machine learning algorithms inspired by the neurons in our brains. They let computers complete tasks like recognizing images, handwriting recognition, and other forms of pattern recognition that machines can use to make predictions.

Support vector machines (SVMs) are another machine learning algorithm. These designs are for computer vision, which is the science of how computers can detect, receive, and process images. In a support vector machine model, there’s one variable being predicted from many different inputs. The goal of SVMs is to find a hyperplane that best separates the input data into two distinct sets.

Decision trees, random forests, ANNs, and SVMs are examples of algorithms that can make accurate predictions. These tools work well with large datasets; however, they require careful preparation and data feeding (known as “feature engineering”).

Explore and learn Data Science with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. Learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization using Tableau. This PG program is for industry professionals to help students master real-world Data Science applications from the ground up. Therefore construct strong models to provide meaningful business insights and forecasts.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Wearable scanners boosting productivity in supply chain operations is the future

Posted on by Imarticus Learning

Did you know that warehouse wearable technology has created a paradigm shift in how supply chain operations function? So, before you set sail on a supply chain management career, here’s a snapshot of how wearables in the warehouse are the future.

Wearables and accompanying apps have been in use for quite some time in warehouse operations. These are devices that are connected to the internet and worn on the head, face, wrist, or other body parts for hands-free communication. They are pretty much like activity and fitness trackers, but in the case of warehouse wearables, the technology helps workers perform supply chain tasks without having any additional mobile device in tow. 

Why Wearable Technology In the Supply Chain Is the Future

Wearable tools in supply chain operations include devices for hands-free scan verification, picking, and sorting. These devices have been improving warehouse productivity for decades and are here to stay for the foreseeable future. 

But what exactly are the benefits they confer on supply chain operations? Let’s find out.

Increased Productivity

Using wearable devices and freeing up employees’ hands dramatically enhances the speed of packing, picking, and shipping operations. Moreover, there is a significant increase in productivity when pickers can scan barcodes, receive instructions, and update inventory information without using a conventional handheld scanner or pen and paper.

Improved Accuracy

Wearable devices like scanners can reduce errors significantly since they eliminate the need for manual data entry. Furthermore, voice-directed wearable devices use voice commands over a headset to instruct employees about picking locations, SKU information, etc. These, along with hands-free barcode scanners and other automatic identification technology improve accuracy and increase productivity.

Identification of Inefficiencies

Awareness of the inefficiencies in the supply chain operations is as important as making the process more efficient. Identifying the obstacles that slow down processes make it easier to eliminate the hurdles. Wearable technology logs valuable information that can be helpful to identify inefficiencies and devise solutions. 

Improved Safety and Ergonomic Benefits

Most often, warehouse employees perform repetitive tasks that can cause injury and fatigue. Hence, warehouse wearables are designed to minimize the number of workplace injuries. For instance, these devices can monitor employees’ location, physical activity, and health, and if any unsafe activity or posture is detected, they provide real-time feedback to the wearer.

Enhanced Speed

Last but not least, the increased awareness that hands-free devices offer allows employees to complete their jobs more quickly. Using mobile computers while doing supply chain tasks is inconvenient, takes up time, and slows down the workflow. But wearables ensure there are minimum disruptions in the workflow, which, in turn, increases the speed of output. Thus, employees can cut down on the extra time wasted. 

Wearable solutions have made life in the warehouse much easier. Moreover, the evolution of machine learning and artificial intelligence has generated further value in the warehouse through robotics, computer vision, natural language processing, and similar sub-technologies. Overall, wearable solutions have transformed supply chain operations by boosting employee productivity and improving efficiency.

What To Look For In A Supply Chain Management Course?

Looking for a supply chain management online course? Well, there are several online courses that cater to the needs of professionals aspiring to make a career in the field of supply chain management. But before you settle for an SCM course, ensure that it has a robust curriculum incorporating key programming languages and tools like Python and Excel and hands-on learning with real-world projects.

How are Business Risks Predicted using Logistic Regression?

Posted on by Imarticus Learning

Logistic regression is a mathematical technique that estimates the probability of an event occurring. Using historical data to create a predictive model, you can use regression to predict business, investment, operational, and strategic risks. By understanding how these risks get indicated, you can better assess your company’s vulnerabilities and protect them from future losses.

This blog post will provide examples of how you might use regression in your workplace and explain what this technique does in more detail.

Why is Logistic Regression critical?

It is a statistical technique that tries to understand how the probability of an event occurring changes when one or more variables get altered. The method builds predictive models using data about previous incidents to use for proactively predicting future events. For instance, you could use regression to guess which customers are most likely to stop using your products and services.

Logistic regression can use to predict business risks in many ways, including:

  • Identifying the likelihood of a bad debt written off.
  • Assessing the probability that an IT system will cause downtime.
  • Estimating the risk that a new product or service will flop.

For example, suppose you are assessing the risk that a customer will default on their repayments. In that case, your model might include variables such as the loan amount and the borrower’s age. If you are trying to assess IT downtime risk, some variables might be how old a system is and its many users.

  • Assessing internal risk levels by quantifying how much staff turnover there has been over the past year. By using information about the average time, it takes for employees to complete their tasks.

For example, suppose you are trying to determine which product is most profitable. If you are trying to assess how quickly tasks are completed, some variables might be how long a study takes to complete and how many times it has met before.

  • You can use it to quantify the risk that you will not receive payment for goods or services supplied.
  • Assessing the likelihood of a customer is likely to leave your company’s favor based on variables. Such as their tenure, monthly spending, and how many requests they have made for support.
  • Predicting the probability of a new product being successful.
  • It determines the likelihood of a new employee bringing in a valuable new business.

Explore and learn with Imarticus Learning

This PG program is for industry professionals to help students master real-world applications from the ground up. Therefore students can construct strong models to provide meaningful business insights and forecasts.

This program is for recent graduates and early-career professionals who want to further their careers in Analytics, the most in-demand job skill. With this program’s job assurance guarantee, students may take a significant step forward in their careers.

Some course USP:

  • Risk management courses aid the students in learning job-relevant skills that prepare them for an exciting financial market career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Understanding the Differences between Linear & Logistic Regression

Posted on by Imarticus Learning

Linear regression is one of the most common types of statistical models used in data prediction. It’s easy to use, and it assumes that a straight line can express a relationship between two variables. Logistic is another type of statistical model that also makes predictions.

Still, it does so using a logarithmic function rather than a linear function. This post will explain how these two methods differ and give you some practical applications for them both.

What is Linear Regression?

The linear discriminant analysis uses when predicting one variable based on another. This model assumes that if the slope of the line changes, so does the value of the predictions. It fits a straight line to your statistics using an ordinary least squares method. If you have more than two variables, consider using multivariate instead.

What is Logistic Regression?

Suppose you have an extensive collection of data that includes three different variables, one of which is the outcome you’re trying to predict (label). Using Linear might not make sense if your result isn’t directly related to the other two variables (independent variables or features).

Logistics can easily handle non-linear relationships between the label and the elements. For example, if you’re trying to predict if an email is a spam or not. The probabilities generated by logistics can work even if your features are unrelated to each other.

The Differences between Linear & Logistic Regression

LINEAR REGRESSION LOGISTIC REGRESSION
It is a simple and robust method based on linear algebra. It is one of the most well-known machine learning algorithms.
It assumes that the relationship between features and the outcome is linear, making it ideal for modeling or analyzing existing relationships in data. It is for binary classification problems, but you can also use it to solve multi-class classification problems by using an approach called “one versus all.”
You can, however, add interactions between variables to create non-linear models such as quadratic functions. You can not add interactions between variables to create non-linear models.
Unlike logistic, linear doesn’t require the outcome to be known beforehand.

 

 Logistic gets considered a supervised algorithm because you must provide the predicted (outcome) label beforehand.
 It is excellent at handling these events because it only cares about the relationship between features and outcomes. Logistic, on the other hand, is designed for discrete data (categorical or binary). It can’t handle these cases effectively because of how the sigmoid function works.

 

The table summarizes some of the critical differences between linear and logistic. Notice that, like all machine learning models, both algorithms are affected by outliers or data points outside the normal range (tails) that generate significant errors.

Explore and learn with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. With Tableau, a student can learn Python,  SQL, Data Analytics, Machine Learning, and data visualization. The curriculum includes Capstone Projects, real-world projects, relevant case studies, and mentoring to assist students in becoming a master in their fields.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

How Data Scientists Make Data-Driven Decisions using Logistic Regression

Posted on by Imarticus Learning

A data scientist is a person who uses statistics and information technology to analyze data, identify patterns, and generate insights. They use sophisticated algorithms for these purposes. This blog post will cover logistic regression and how to apply it to your business problems effectively!

What is Logistic Regression?

It is a machine learning algorithm that means that the results are learned from a training set and used to make valuable predictions about unseen data. In the case of regression, those predictions are probabilities.

Some Challenges of using Logistic Regression?

The regression and other algorithms can be challenging to interpret and may only provide a probability between 0 and 1.

For instance, if we feed in a set of data for people who have been diagnosed with cancer, the algorithm will learn which variables are most important for predicting that diagnosis.

However, it will give us an output representing the probability that a patient has cancer. This number does not necessarily mean that the person has or doesn’t have cancer — it is simply the probability we can use to make an informed decision.

How is data science used to make a data-driven decision?

One of the most significant impacts data science has today can be seen in its use as a tool for business decision-making.

Predictive modeling and regression are two popular techniques that many companies have adopted across all industries because they empower businesses to make more accurate decisions, resulting in greater efficiency.

Logistic does this by taking historical data and learning which variables are most helpful in making predictions.

The future of Data Science?

  • The future of data science lies in developing new techniques that can build on these existing methods while overcoming their limitations.
  • It also depends on working effectively with vast volumes of data from various sources, such as sensors, images, and video.
  • One of the most talented research areas is the development of techniques that can learn on their own – without being fed historical data to train them first.

Explore Data Science with Imarticus Learning

Corporate experts gave input for this postgraduate program to help students master actual Data Science applications. Be it grounding up or constructing solid models to provide meaningful business insights and forecasts. This course helps you learn all. The program is for early-career workers (0-5 years) who dream of growing and building data science careers.

With this program’s employment guarantee, the student may take a significant step forward in their career. After satisfactorily finishing the program, students assure interview chances.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions.
  • The program helps students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Why do organizations emphasize on delivering business value through efficient procurement and logistical management?

Posted on by Imarticus Learning

Looking for the best logistics and supply chain management course on the block? Before you choose the supply chain Management course that suits you, understand why efficient procurement and logistical management are vital for organizations in the first place.

Procurement and logistics are two of the most common terms that you will hear in a business environment. While procurement refers to sourcing supplies, logistics means the holistic process of acquiring resources from their origin, managing them, and transporting them to the point of consumption.  

But why do organizations emphasize procurement and logistics management to deliver business value? Let’s find out.

Why is Procurement Management Important?

Procurement management entails overseeing all the processes involved in sourcing the materials, products, goods, and services required for business operations to run efficiently. The terms “purchasing,” “sourcing,” and “procurement” are often used interchangeably to describe the procuring of supplies and management of the process. However, it is “procurement” and “purchasing” that essentially refers to the operational function, while “sourcing” is usually more strategic.

Procurement management has a direct impact on strategic business operations and the company’s bottom line for the following reasons:

  • Procurement management ensures the proper acquisition of items and services so that processes and projects can proceed efficiently.
  • Procurement management can be instrumental in sealing valuable production and supplier contracts.
  • It can also set the stage for new and innovative processes and help domestic and local businesses go global.
  • When optimized, procurement management can save time, money, and resources while offering a competitive advantage.
  • By actively pursuing diverse suppliers, procurement management can be at the forefront of trailblazing CSR initiatives to promote diversity and inclusion.

Why is Logistics Management Important?

What earlier used to be a term restricted to military operations has now become a widespread business concept. Today, the business sector uses the term logistics to describe the efficient flow, storage, and management of goods from their source to the point of consumption.

Here’s how logistics management is critical to stay competitive and keep pace with customers’ demands:

  • Managing logistics significantly reduces overhead costs, increases productivity, and ensures faster delivery. 
  • Logistics is the foundation of a successful supply chain. It helps boost the sales and profit of businesses dealing with the production, warehousing, shipment, and delivery of products. 
  • The analysis of a company’s supply chain management data improves efficiency. Using a transportation management system to track the movement of goods in and out of a business optimizes the process and minimizes disruptions. 
  • Well-managed and organized logistics ensure fast and seamless product delivery to customers. 
  • Last but not least, efficient logistics management improves customer service, meeting and exceeding customer demands. It enhances the brand image and helps the business attract and retain long-term customers.

What to Look For in a Logistics and Supply Chain Management Course?

If your goal is to get a professional certification in Supply Chain Management and Analytics, there are several online courses that you can choose from. While each of them claims to be the best, ensure that the program caters to your professional needs.

For example, the course curriculum should cover key programming languages and tools such as Python, Excel, and TOra, and hands-on training in real-world SCM projects. Overall, the course must be robust enough to prepare you for the operations and supply chain industry with a cutting-edge curriculum and industry exposure.

Why artificial intelligence and machine learning is the new blue print for the data science industry

Posted on by Imarticus Learning

Data science is a broad discipline concerning data systems and processes with the aim to maintain data sets and derive insights from them. On the other hand, Artificial Intelligence (AI) pertains to mathematical algorithms that can replicate human thought processes to understand complex relationships, plan for the future and make actionable decisions.

Machine Learning (ML), on the contrary, helps to implement AI by “training” computers to solve various tasks. Data science incorporates several areas of artificial intelligence and machine learning while primarily focusing on gaining insights from data.

But, how are these three fields related, and what is the impact of AI and ML in shaping the data science industry? Let’s find out.

Data Science, AI, and ML: Where Lies the Difference? 

Data science finds widespread use in several businesses to improve production processes, innovation of product design, and enable strategic planning. It involves techniques of mathematics, statistics, computer science, and even ML to extract knowledge from data and provide insights and decision paths. 

On the contrary, AI enables computers to observe their environment and make decisions based on what they observe. Some of the most widespread uses of AI include processing clinical data, creating chatbots and smart assistants, and financial planning. Add the machine learning component, and AI can enable computers to solve new problems such as classification and predictions.

The fields of data science, AI and ML overlap significantly and yet have subtle differences. In a nutshell, data science gives insights, AI produces actions, and ML facilitates predictions.

The Combined Effect of AI and ML on Data Science

Data science and data analytics have long been revolutionizing the business landscape. Companies that have mastered their use of data science and analytics aim to delve deeper into data to increase efficiency, boost their bottom lines and gain a competitive edge.

Thus, they are looking to incorporate AI and ML into their data infrastructure to achieve business goals. For instance, call centers have long been using conversation analytics software, platforms that leverage AI and ML to gain better data insights. 

Following are a few more examples to show how AI and ML combined with data science make a remarkable difference to organizations:

  • Conversational AI systems such as chatbots and smart assistants engage in highly interactive conversations with customers and users and capture actionable user insights in the process. 
  • Predictive analytics applications enable the analysis of dynamic datasets to make financial predictions, forecast business trends, customer behavior, etc. 
  • Hyper-personalization systems enable customized offerings to customers, such as product recommendations, targeted advertising, personalized medical care, and financial planning. 
  • Also, organizations can consistently respond to evolving threats, thanks to anomaly detection systems that leverage the potential of adaptive fraud detection and cybersecurity processes.

The business value of data science alone cannot be understated. However, integrating it with the tools and techniques of AI and ML has way more potential to produce actionable insights from the ever-expanding data pool. In conclusion, AI and ML have been impacting the data science industry for a long time and will continue to do so in the foreseeable future with even more ground-breaking innovations.

How to Learn AI?

Looking for an artificial intelligence course? There are several AI and ML courses available online with extensive coverage of data science, ML, NLP, deep learning, and computer vision. But before you settle on a course, ensure that the curriculum offers practical learning through real-world projects, has scope for ample industry exposure, and provides a globally recognized certification after course completion.

Why Linear Regression is Important for Data Scientists & How to Learn It?

Posted on by Imarticus Learning

Linear regression is a powerful predictive modeling technique that enables the statistical analysis of continuous variables. It is the most popular technique for estimating relationships between inputs and outputs.

This post discusses linear regression, how to use it in data science, and why you need to know about it as a professional data scientist.  Now let’s dive into the topic!

What is Linear Regression?

We start this section by defining linear regression. Here, in simple words, it is an approach to estimate the relationship between the input and output. It simplifies the modeling process and produces more interpretable results. When you need to make predictions on new data, Linear discriminant analysis is a better option for making predictions on new data points (i.e., test set) because of its solid statistical foundation and mathematical proofs of performance guarantees.

Why is Linear Regression Essential for Data science?

For a Data Scientist, it is essential to know and understand the concept of linear regression and how to use it. This section provides some reasons why it is critical for data scientists:

When you don’t know which variables are important: In many real-world problems, no one tells you which input variable(s) affect the output variable. In cases where you have access to historical data, it is possible to find the relationship(s) between input and output variables (i.e., linear regression).

When your model needs linearity assumption: Incorporating nonlinearities in the prediction function requires complex modeling techniques like applying polynomial transformations or neural networks.

How can we use linear regression?

Here are some common scenarios where we use in the industry.

  • You can predict the price of a house/cars/robots etc., indicating loan eligibility for an individual based on his salary. How many items will you sell tomorrow? What time of the day am I likely to buy something?
    Estimating Expected Weight of a baby based on mother’s weight during pregnancy, Estimating the passengers who will purchase tickets for an airline, etc.
  • Now you can solve all these real-world problems with linear regression!
  • Linear regression is a beautiful yet straightforward statistical technique to estimate the relationship between input and output variables. In other words, it helps you to find a function that best explains the relationship between input and output variables.

Input features = house size, car speed, age of a person, flight duration, etc

Output variable = price of a house/car/flight ticket etc

Explore Data Science career with Imarticus Learning

Students can master the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies. With Tableau, you can learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization. With this program’s job assurance guarantee, students may take a significant step forward in their career.

Some course USP:

  • This Data science courses with placement assurance aid the students to learn job-relevant skills that prepare them for an exciting career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon.

The future of artificial intelligence and machine learning in the Biosciences

Posted on by Imarticus Learning

Do you know why artificial intelligence courses are so popular? For the last 70 to 80 years, we have been trying to simulate our intelligence in many artificial entities, which has given rise to the growing field of artificial intelligence (AI). Although AI has surpassed humans in many respects, it still does not live up to its name. AI, as we define it, does not yet exist, nor is there a consensus among experts as to whether it can be achieved.

However, while AI is captivating with its incredible applications and rapid growth (autonomous cars, nanorobots, etc), AI has infiltrated almost all disciplines and has had a particular impact on biosciences. AI offers sufficient computational power and capacity to address the complexity of biological research through simulations (known as “artificial life”). It presents itself as an ideal testing ground, a bounded but unbounded environment where physical laws are adaptable, all parameters are traceable, measurable, storable and retrievable.

AI in Biology

This translates into the possibility of overcoming some of the most important challenges of research in biology. For example, the ethical limits of animal experimentation with drugs for cancer and other diseases, or the methodological difficulties in studying complex systems such as human language, multicellularity or collective intelligence. AI also benefits from this interaction. After all, the key to being able to reproduce a natural system in an artificial environment depends on the knowledge one has of the system in question.

Deep Learning

Deep Learning is one of the many approaches to AI and is inspired by the structure and functioning of the brain through the interconnection of neurons, mimicking the biological structure of the brain through algorithms called Artificial Neural Networks that specialise in detecting specific features, through different layers of neurons, to achieve unsupervised learning. The concept is given by the multiple layers it can comprise.

A neural network needs approximately 50,000 times more energy to function than the human brain. For this reason, computers with traditional architectures are not suited to support the parallel processing that the brain carries out so efficiently. Therefore, research is being carried out into brain-mimicking computing techniques called Neuromorphic Computing.

Artificial Immune Systems

There is an initiative that aims to understand how different parts of the brain work in order to diagnose and treat brain diseases and to develop neuromorphic computers that can learn in the same way as the brain does. These advances need to incorporate multidisciplinary knowledge from neuroscience research, psychology, and ICTs. But it is not only the human brain that is a source of inspiration. Artificial Immune Systems comprise computational methods based on the processes and mechanisms of the human immune system and are used for learning and protecting information systems from malware.

AI and IOT

Finally, we could compare the relationship between Artificial Intelligence and the Internet of Things as the relationship between the brain and the human body. Our bodies collect sensory information (sight, hearing, touch, etc) and send it to the brain, to make sense of this information in order to make the decisions and/or actions, sending signals back to our body if necessary, for example, to pick up an object.

Conclusion

In conclusion, the symbiotic relationship between AI and bioscience has provided the ultimate testing ground for solving some mysteries of biology, as well as the theoretical framework needed to achieve real artificial intelligence. Any of us can learn AI or do a machine learning certification, but only the best prepared will be part of this amazing field of study, so study with Imarticus and go as far as you want.

Get a free 1:1 Imarticus counselling
Get a free 1:1 Imarticus counselling