IT teams around the globe continue exploring AI & ML technology to enhance efficiency and drive productivity. Both AI and ML are based on statistics and mathematics, but they are not the same thing.
So, what is the core difference between Artificial Intelligence and Machine Learning and how can they help companies in software development?
Artificial intelligence definition
Artificial intelligence is the ability to replicate human intelligent behavior and perform human-like functions through computer-based software. AI performs tasks that require human intelligence such as reasoning, thinking, learning from experience, and the most important thing - making its own decisions.
Artificial Intelligence surrounds us and is an active component of our day-to-day lives. Google search, Facebook feed, booking a hotel online, AI takes an active role in all of this, processing our activities in the background.
What are some of the benefits of Artificial Intelligence?
Only artificial intelligence can recognize faces and voices, sort out images and solve more "creative" tasks. There are various types of Artificial Intelligence that are used in automotive production, predictions of goods demand, blocking suspicious bank activity, client-based services, healthcare, and other industries.
Some of the most common industries, where AI is being actively implemented is:
Industrial robotic system is one of the examples of successful AI implementation. Thanks to AI industrial robots monitor accuracy and performance. They have an ability to identify when maintenance is required, helping avert downtime. An example of artificial intelligence technology is implemented in robot Sophia.
Personal assistant tools are built-in Human-AI interaction gadgets.
The most well-known intelligent personal assistants are Google home, Siri by Apple, Alexa from Amazon, Cortana by Microsoft.
Artificial intelligence can make contact centers more user-friendly. Machine programs can respond to complex customer requests, allowing timely responses and reduced delays. Many companies are now replacing helpdesks with AI-powered chatbots, and this has already yielded positive results.
For example, chatbots are used in online stores: the buyer receives a response faster, and the seller does not waste time on a regular chat.
In the context of the COVID-19 pandemic, augmented reality (AR) technology has received new momentum thanks to its remote communication capabilities for training experts. The consumer AR market is predicted to be more dynamic in 2021. A relevant augmented reality example is a well-known social network. Facebook is expected to release smart AR glasses as part of the Reality Labs initiative. Among the factors positively influencing the development of AR, it is worth mentioning remote support caused by the rules of social distancing.
So, how exactly does augmented reality work? Think of the AR glasses with an option that can dim the real world, making the augmented reality content be more visible, and has an increased field view. The newest innovative product from Magic Leap targets the enterprise section, such as manufacturing, healthcare, defense, and public sectors.
VR technology will become really mainstream and will compete with traditional TV, displays, or smartphones. In the corporate environment, where VR technology is used primarily used for training and simulation is being successfully implemented. During and after the pandemic, remote training and support for employees using Virtual Reality allows them to stay involved with limited disruption to the workflow in most cases. But nonetheless, the consumer still represents the largest potential VR user base. VR momentum is growing in both consumer and enterprise markets.
Artificial Intelligence is proving to be a game-changer in the healthcare industry. Starting from AI-assisted surgeries to private records protection, AI in healthcare is giving the industry much-needed improvement. AI helps eliminate high healthcare costs and inefficient processes.
AI-based virtual assistants are reducing nurses’ workload by 20%, helping doctors save 17% more time on their schedules. Some companies are using AI to analyze clinical data to discover gaps in a patient’s treatment. Concierge-like services help patients book appointments and make payments online. In addition, patients can chat with a nurse or a doctor via video calls and have prescription forms filled.
What is the difference between AI & ML?
In conclusion to the artificial intelligence article, AI solves tasks that require human intelligence while ML is a subgroup of artificial intelligence that solves specific tasks by learning from data and making predictions. Machine learning algorithms use historical data as input to predict new output values.
Is Machine Learning important?
ML gives enterprises trend predictions in customer behavior and business operational patterns, as well as the development of new products.
Companies like Google, Facebook, Uber, put ML in the center of their operations. Machine learning has become a significant competitive advantage for many companies.
What is Machine Learning and how is it used?
Machine learning techniques range from predicting customer behavior to forming an operating system for self-driving cars. ML can help enterprises understand their customers' behavior at a deeper level. By collecting customer data and corresponding it with behavior predictions over time, ML algorithms learn associations and help teams craft product development and marketing initiatives to customer demand.
Some companies use ML as a primary driver in their business models. Uber uses algorithms to match drivers with the customers. Google uses machine learning to drive advertisements in searches.
The most known ML application is Facebook’s newsfeed engine. Machine learning is using augmented engine and known pattern behavior of a user, to personalize how each member’s feed is delivered. For example, if a user regularly reads posts from a certain group, Facebook will start showing more of the group's activity in the user's feed. If the user changes reading preferences, the news feed will adjust accordingly thanks to ML technology.
- Customer relationship management. CRM software use machine learning to analyze emails and let team members respond to the most important messages first. Advanced systems recommend potentially effective responses.
- Business intelligence. BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points, and anomalies.
- Human resource information systems. HRIS systems use machine learning models to filter through applications and determine the best candidates for an open position.
- Self-driving cars. Machine learning algorithms allow a semi-autonomous car to recognize a partially visible object and alert the driver.
- Virtual assistants. Smart assistants typically combine supervised and unsupervised ML models to interpret natural speech and supply models.
- E-commerce websites have machine learning tools that provide recommendations of different products based on historical data. ML makes recommendations based on what a customer has liked, added to the cart, and other related behaviors.
According to the “Are Organizations Succeeding at AI and Machine Learning?” survey, AI & ML implementation fails due to a lack of data and internal resources.
In 44% of all cases, pilot projects in the field of AI and ML were successful, 22% of projects are in the testing phase. 34% of all projects failed or were rejected at the idea stage.
The main factors of failure are:
- 34% lack of experience in the company
- 31% lack of data ready for production
- 31% a poorly developed strategy
How can AI & ML accelerate product development?
Successful AI/ML implementation will help companies establish leading positions in the industry. Companies and enterprises that have successfully implemented AI & ML technology, report increased productivity (28%), improved customer satisfaction (27%).
This data is especially relevant for businesses working in:
- Systems operations & Management
Companies that aim to succeed in AI/ML implementation need to determine whether they will build internal AI & ML support or use outsourcing talent from a reliable partner. The risk of implementation failure is high, that’s why many companies choose to rely on a trusted partner to help them overcome the risk and complexity of AI & ML development.
At Edsson, we help businesses determine what areas would benefit from using AI & ML technologies. Our data science teams possess deep knowledge and machine learning best practices, deep learning, and extensive experience to facilitate improved efficiency through AI & ML.
Choosing the right ML model is critical to solving a company’s problem. Our experts start by outlining a comprehensive strategy for the right tool implementation. Aligning the problem with potential data inputs. This step requires help from data scientists and experts who have a deep understanding of the problem:
- Collecting data, formatting, and labeling the data if necessary.
- Choosing what algorithms to use and testing its performance.
- Carefully measuring outputs until they reach an acceptable level of accuracy. This step requires expertise from data scientists with feedback from experts who have a deep understanding of the problem.
What does the future hold?
Machine learning platforms are giving companies accelerated opportunities for competitive advantage. They cover a wide range of activities such as data classification, model building, data collection, and application deployment.
Today ML tools increase the importance of business operations and are becoming crucially important in intensifying software development and creating future-proof products.