How AI Can Help Life Science Companies

by | Jul 27, 2022 | Artificial Intelligence, Digital Marketing, Life Science Marketing

What is AI/ML and Why Does it Matter to Life Sciences?

Artificial Intelligence (AI) and Machine Learning (ML) represent an important evolution in computer sciences and data processing that is rapidly transforming a vast array of sectors including healthcare, finance, manufacturing, transportation, retail, education, energy, marketing, life sciences, media, telecommunications, insurance, government and others. Today we’re going to try to summarize what AI is and review some examples of AI in life sciences—and other industries.

As life sciences businesses and other organizations undergo digital transformation, they face a growing tsunami of data – both structured and unstructured – that is at once incredibly value and increasingly burdensome to capture, process and analyze. New tools and methodologies must be developed to manage the vast quantity and variety of data being collected, mined for insights and acted upon when they’re discovered. These technologies allow us to automate processes, make decisions and solve problems faster and better than ever before.

The power of ML and AI In life sciences lies in their ability to learn from data, making them ideally suited to address some of today’s most pressing healthcare business challenges such as fraud detection, customer experience management, risk assessment, supply chain optimization, product development, clinical decision support, predictive maintenance, cybersecurity, among many others.

What is AI?

Artificial Intelligence, or AI, is a broad term that describes any process that simulates human thought or behavior. In simple terms, it includes machine learning, neural networks, natural language processing, expert systems and robotics. A general definition of artificial intelligence might go something like this: “AI is what makes computers think.” But there are many nuances to the field, and different people use the term differently.

For example, some people refer to machine learning as AI because it involves data and statistics; others call it part of AI because it uses statistical models. Some researchers believe that the distinction between AI and machine learning is becoming less relevant as technology progresses and advances. Others argue that the difference is important. And still others say that the distinction doesn’t matter at all.

In fact, one of the main reasons why the term AI is so confusing is that it encompasses multiple technologies and fields of study. For example, while machine learning is often considered a subset of AI, it is actually quite distinct from it. More on that in a bit.

Types of AI

AI is becoming increasingly important in our day-to-day lives. From Alexa answering questions to Siri helping us make phone calls, we’re seeing more and more intelligent technology being used every day. But what exactly does AI entail? There are many different forms of artificial intelligence (AI), each one with its own unique characteristics. Here are the four main categories of AI you’ll see today.

Reactive Machines

A reactive machine is simply a computer program that performs specific functions or sets of functions based on certain conditions. For example, a weather forecasting app might use a reactive machine to determine whether or not rain is likely to fall within the next 24 hours. This type of AI is very simple and doesn’t require much processing power. It’s often referred to as dumb AI because it requires little or no learning.

Examples of reactive machines in life sciences include apps that predict how long someone will live based on factors such as age, gender and lifestyle habits. Other examples include chatbots that can answer basic questions about your health or financial status.

The pros and cons of reactive machines are that they are easy to implement and don’t need much training. However, they aren’t able to adapt to new situations or change over time. They also have limited capabilities compared to other types of AI.

Probabilistic Machines

Probabilistic machines are also known as Bayesian machines, which are programs that perform tasks using probability theory. They rely on mathematical formulas called probabilistic algorithms to calculate the likelihood of an event occurring. These algorithms are typically used for things like predicting the outcome of sporting events, analyzing DNA sequences and performing medical diagnosis.

An example of a probabilistic algorithm is the Naive Bayes classifier, which is used by many spam filters. Another example is the KNN algorithm, which is used to classify images into groups.

The pros and cons of probabilistic machines include their ability to learn and adapt to changing environments. The downside is that they take longer to train than reactive machines. Probabilistic machines also tend to be more complex than reactive machines.

Neural Networks

Neural networks are another form of AI that has been around since the 1950s but only recently become popular again. Neural networks are essentially large collections of interconnected nodes that work together to solve problems. Each node represents a single concept or piece of information. Nodes are connected to other nodes through weighted links, allowing them to communicate with one another.

In neural network terminology, these connections are called synapses. A synapse is basically a connection between two neurons that allows them to exchange information. Neurons are the building blocks of the brain, and they process information by passing signals along to other neurons.

Examples of neural networks include deep-learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN). CNNs are designed to recognize patterns in data while RNNs are good at recognizing sequential patterns.

The pros and cons of neural networks include their ability to learn from experience and adapt to changing environments without being explicitly programmed. The downside is that neural networks are extremely complicated and difficult to program. This makes it hard to create neural networks that can handle all kinds of different scenarios.

Limited Memory AI Systems

Limited memory AI systems are capable of storing information about things like previous interactions with the environment. They can learn from those experiences and adjust accordingly. A good example of this is a chatbot that learns how people respond to certain phrases or words. If someone asks the bot a question, it responds appropriately. Over time, however, the bot will begin to understand that people don’t always ask questions in the same way and will change its response accordingly.

Examples of limited memory AI systems in life sciences include software that predicts how well a drug will work against a disease based on previous research results. The system could then use these findings to create new drugs that could potentially be effective against other diseases.

The pros and cons of limited memory AI systems are similar to those of probabilistic algorithms. Limited memory AI systems have an advantage over probabilistic algorithms because they’re easier to build and maintain. However, they aren’t as flexible as probabilistic algorithms.

Theory Of Mind AI

In theory of mind AI, computers are able to think like humans do. They can observe their surroundings, process information, and even make predictions based on that information. Think of it as a sort of meta-intelligence. Theory of mind AI is still pretty far away from reality, though. Right now, there isn’t anything close to human-level theory of mind AI.

A theory of mind AI could be used for things like understanding what others are thinking and feeling. An example of this would be a chatbot that understands when you’re upset and tries to cheer you up. In addition, a theory of mind AI could also be used to detect lies. Imagine if a chatbot was trained to recognize when someone was lying. That could be useful for detecting fraudsters and liars who try to hide their true intentions.

The pros and cons of theory of mind AI are similar to those of neural networks. It’s possible to train a computer to understand what another person is thinking and feeling, but it’s very difficult to teach a computer to understand why someone thinks or feels the way they do.

Artificial General Intelligence

Artificial general intelligence is the holy grail of AI. Researchers have been trying to achieve AGI for decades, but they’ve had limited success. So far, most attempts at creating an AGI have failed. However, recent advancements in deep learning may be changing that. Deep learning is a form of machine learning that allows machines to perform tasks by analyzing large amounts of data. The hope is that someday, we’ll create an AGI using deep learning.

We already have some rudimentary forms of AGI today. For example, Google Translate uses neural networks to translate text between languages. DeepMind created AlphaGo, a supercomputer that plays Go at a level comparable to professional players.

Pros and cons of artificial general intelligence are similar to those of neural nets. There are many reasons why it hasn’t been achieved yet. One reason is that it’s extremely hard to program a computer to think like a human being. Another reason is that it takes a lot of computing power to analyze all the data needed to create an AGI.

Machine Learning vs. Deep Learning

There are two subcategories of AI—machine learning and deep learning. Machine learning is a type of AI where software learns without being programmed. It can be used to teach machines how to perform tasks by feeding them examples of correct answers. This is also known as supervised learning.

Deep learning is another type of AI which is based on using neural networks. Neural networks are computational models inspired by biological neural networks. They have been successfully applied to a wide range of pattern recognition tasks such as handwriting recognition, optical character recognition, speech recognition, translation, image classification, etc.

Deep learning has recently become popular due to its success with computer vision applications. Computer vision refers to the branch of AI that deals with visual perception. The goal of computer vision is to build intelligent robots that understand images and video.

How Does AI work?

When we talk about AI In life sciences, we usually mean machine learning. Machine learning is an umbrella term for several techniques that let computers learn without being explicitly programmed. These include reinforcement learning, genetic algorithms, evolutionary computation, fuzzy logic, association analysis, clustering, and Bayesian inference.

The basic idea behind machine learning is to provide a system with enough information to make decisions. This could be anything from identifying patterns in your email inbox to helping you find the best restaurant in town.

Machine learning works by analyzing large amounts of data and making predictions based on that data. For example, if you’re looking for a new job, you may want to know whether someone else who worked at the same company was able to get promoted. You would need to collect lots of data about previous employees, including their performance reviews, salary history, and education level. Then, you’d analyze the data to see what factors correlated with promotion. From there, you could use this knowledge to predict whether or not you’ll be successful.

What Are Real World Examples of AI in Use Today?

AI is already changing our lives today. In many cases, it’s improving things we take for granted. Here are just a few ways AI is transforming our world:

1. Self-driving cars

Self-driving cars are one of the most exciting uses of AI. Google has been working on self-driving vehicles since 2009. Today, they have over 1 million miles driven autonomously.

2. Chatbots

Chatbots are programs designed to simulate human conversation. Companies like Facebook, Microsoft, Amazon, and Apple all offer chatbot services.

3. Voice search

Voice search is becoming more common every day. According to Cisco, voice searches will grow 50% annually.

4. Virtual assistants

Virtual assistants like Siri and Alexa are great tools for getting things done. But did you know virtual assistants actually use AI technology?

5. Image recognition

Image recognition allows us to identify objects in photos and videos. We’ve seen some pretty cool examples of this, but the future looks even brighter.

6. Recommendation engines

Recommendation engines recommend products to customers based on their browsing habits. If you buy shoes online often, then a recommendation engine might suggest shoe stores near you.

7. Translation

Translation software can translate text between two languages. It can also translate audio files into different languages.

8. Natural language processing (NLP)

NLP helps machines interpret natural language. DeepMind used NLP to create a computer program that beat humans at Go.

9. Automated writing

If you have ever written an email, report, or tweet, then you’ve used automated writing. Many companies now hire writers, but these aren’t always human. Some use NLP to write articles or tweets.

10. Speech recognition

Speech recognition allows people to interact with computers using only their voice. Marketers are starting to use speech recognition to build better customer experiences.

11. Machine vision

Machine vision gives cameras the ability to recognize images and video. They can tell objects apart as well as differentiate colors and textures.

12. Text analytics

Text analytics analyzes unstructured data in documents. These include emails, blog posts, books, and news articles.

13. Augmented reality

Augmented Reality overlays digital information onto your view of the physical world. The technology exists to make shopping easier and more fun!

How Can AI Help Your Business?

There are a number of real-world ways that businesses can use AI with data today, and in addition to specific examples of AI In life sciences listed later on, some of these tangential examples from other businesses might provide you with some great inspiration. So, let’s explore a few of them here.

Machine Learning and Artificial Intelligence are becoming increasingly important parts of how organizations operate. They’re helping companies understand customer behavior, predict future trends, and improve overall efficiency. However, many people don’t know where to start. So we’ve put together a list of some of our favorite examples of how businesses are leveraging Machine Learning and AI today.

Since the advent of artificial intelligence, there have been many advancements made in the field. Machine learning is one such advancement that allows computers to learn without being explicitly programmed. This technology is used across different industries like finance, healthcare, retail, manufacturing, etc. In fact, according to McKinsey & Company, AI could add $16 trillion to global GDP by 2025.

In addition to providing enormous economic gains, machine learning helps companies achieve better customer experience, improve operational efficiency, and develop innovative products. For example, Amazon uses machine learning to recommend books to customers based on previous purchases, while Facebook uses it to identify images of faces and objects, and suggest relevant posts to friends. Here are some of the ways machine learning can help you build a successful life science business.

1. Better Customer Experience

The most obvious benefit of machine learning is improved customer experience. Companies use it to provide personalized recommendations, automate processes, and detect fraud. For instance, Uber uses machine learning to predict where riders want to go next. As a result, it provides faster rides and reduces traffic congestion. Similarly, IBM Watson is able to diagnose diseases like cancer and prescribe treatment plans.

2. Operational Efficiency

Companies use machine learning for everything from improving sales operations to automating billing procedures. For instance, Salesforce leverages machine learning to train chatbots that engage with prospects. These bots can answer questions related to specific products and offer customized solutions. They also make it easier for reps to follow up with leads and close deals.

3. Innovation

Artificial intelligence empowers new innovations. It makes sense then that Google has invested heavily in developing its own AI platform called TensorFlow. By making this open source, Google hopes developers will contribute their time and effort to building even better models.

4. Data Analytics

Data analytics is another area where machine learning plays an integral role. Since companies collect large amounts of data every day, they need tools to analyze it. Deeplearning4j is a tool that lets users leverage deep learning algorithms to perform tasks like image recognition, natural language processing, and sentiment analysis.

5. Security

Machine learning also helps secure networks against cyberattacks. A company called MalwareHunterTeam uses machine learning to protect networks against malicious software (malware). The system collects samples of malware and trains itself to recognize them using big data techniques. Once trained, the system learns what constitutes a threat and eliminates threats before they reach endpoints.

6. Robotics

Machine learning is helping robots become more efficient at performing tasks. Researchers at UC Berkeley built a robot hand that can pick items off shelves or unscrew light bulbs. The robot learns how to complete these actions over time, saving human workers hours each week.

7. Automotive

Self-driving cars are one of the biggest trends in technology. Companies like Uber, Apple, and Tesla have already started producing driverless vehicles. Their goal might be to create fully autonomous cars, though there are still safety concerns as well as regulations to think about.

8. Energy

It’s no secret that we rely on oil and coal to power our cars, trucks, airplanes, and factories. But could renewable energy sources like wind and solar eventually replace fossil fuels? According to some experts, it’s inevitable today. And unlike fossil fuels, artificial intelligence and machine learning can actually improve the efficiency of those systems and make them sustainable.

9. Manufacturing

The manufacturing sector could see a major shift by implementing “internet of things” technologies into production lines. IBM estimated that by 2020, 90% of all industrial machines will be connected to the internet. This would enable real-time tracking and monitoring of equipment and processes, resulting in improved productivity and reliability.

10. Finance

Data analytics and machine learning are becoming crucial components within the financial services industry. Think about automated credit scoring, which allows lenders to evaluate borrowers based on factors such as employment history, income, and debt level. Or automatic investment advice, which gives investors stock recommendations that maximize profit without requiring any expertise.

11. Retail

AI and machine learning can significantly impact retail industries such as grocery stores and consumer electronics retailers. Some startups already offer self-checkout options because face recognition software is cheaper than people. In addition, companies like Amazon use machine learning for recommendation engines and product categorization.

12. Marketing

Marketing automation has been around for decades, but only recently have we begun to see marketing campaigns being orchestrated with machine learning in mind. Marketers from Facebook, Google, and other companies use machine learning to predict customer behavior. They then incorporate this information into their campaigns.

13. Healthcare

Healthcare providers like doctors and hospitals are now leveraging AI tools to analyze medical images, track disease patterns, and prescribe treatment. We may not be able to completely eliminate the need for human doctors, but these new tools provide an additional layer of support when needed.

14. Education

Education platforms like KhanAcademy use machine learning to allow millions of students to learn anywhere at anytime. From basic math to complex engineering concepts, students can now learn almost anything they want from the comfort of their own home.

15. Agriculture

Machine learning applications are helping farmers better understand crops and livestock, thus increasing yields and decreasing costs. Newer, more advanced systems even use deep learning techniques to “learn” how best to grow food through exposure to data.

16. Insurance

According to Accenture, nearly half of global insurance policies contain at least one significant error. These mistakes can lead to huge losses for both business owners and customers. However, advances in cloud computing and IoT can bring cost savings by allowing insurance companies to quickly process claims using big data analytics.

17. Transportation

Machine learning algorithms are increasingly used to design cars, trucks, and airplanes. Self-driving vehicles may soon become reality just like smartphones did, thanks to artificial intelligence. Data collected from sensors, coupled with cameras, radar, and GPS signals, allow machines to identify objects and traffic rules without any input from humans.

18. Life Sciences

The life sciences industry continues to rely on manual labor to run experiments and interpret results. Machine learning algorithms can automate many of these processes, reducing errors and saving time. For example, researchers are developing algorithms that can read genetic sequences and compare them to known mutations. This allows scientists to find new genes or variants associated with diseases.

A Few Examples of Companies that are Working in the AI for Life Sciences Space Today

1. IBM Watson – IBM Watson is being used by scientists to interpret and analyze data.

2. Google DeepMind – Google DeepMind is a division of Google that specializes in artificial intelligence. They are currently working on applications for the life sciences, such as creating treatments for cancer and Alzheimer’s disease.

3. Microsoft HoloLens – The Microsoft HoloLens is a virtual reality headset that was designed specifically for the life sciences, allowing scientists to visualize and interact with data in new ways.

4. Qualcomm Incorporated – Qualcomm is a leading manufacturer of cellphone processing chipsets and other hardware components used in mobile devices across the world. Their deep learning processors are used by some of the biggest names in AI today, including Facebook, Apple, and Google DeepMind (formerly known as DeepMind).

5. Kyton AG – Kyton AG is a German company that specializes in artificial intelligence for health care applications such as diagnosis and treatment planning for diseases such as cancer.

6. Medtronic – Medtronic provides medical equipment that includes artificial intelligence capabilities so doctors can prescribe treatment using machine learning algorithms instead of human judgement alone.

7.Blue Gene/Q Systems LLC- Blue Gene/Q Systems LLC designs supercomputers specifically for use by researchers in various areas of science including life sciences.

Wrapping Up

We’re no strangers to AI/ML and big data. Our business intelligence dashboard consultants recently completed building a full executive level Business Intelligence dashboard for an umbrella organization with nearly 10 sub-brands (and associated data). Built on Microsoft BI, this data warehousing project and dashboard give the company’s executive team and board of directors visibility into nearly every aspect of the company’s sales performance. This has the potential to be the first step in using AI to identify opportunities and make intelligent recommendations for the business moving forward.

Want to learn more about how your business could us AI/ML for sales and marketing? Contact us today!

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