The world we live in is changing rapidly, especially in terms of the advanced technology that is being developed. The outbreak of the COVID-19 pandemic has spurred the speed of adoption of various technologies, especially Artificial Intelligence and Machine Learning.
Artificial Intelligence and Machine Learning are two separate concepts, although Machine Learning is a subset of Artificial Intelligence.
A simpler way to make people understand the nuances is to say that while Artificial Intelligence covers a vast area, Machine Learning constitutes a small portion of it.
The nuances between AI and machine learning
Artificial Intelligence is a field of computer science that uses algorithms like reinforcement learning algorithms and deep learning neural networks, instead of programmes, to mimic human intelligence.
On the other hand, Machine Learning, though also not programmed, uses structured and semi-structured data to analyze the data and give accurate results.
It is a fact that both Artificial Intelligence and Machine Learning were terms first used in the 1950s. However, it is possibly only over the past decade that Artificial Intelligence and Machine Learning have created a deep impact on many spheres. The innovation in this field has also increased several folds.
The development of new technologies and applications using Artificial Intelligence and Machine Learning is responsible for the ever-changing face of not just science and business, but also everyday life.
For instance, after the outbreak of the COVID-19 pandemic, Artificial Intelligence was used by researchers to improve the speed of vaccinations and provide treatment to people. Similarly, Artificial Intelligence was used to equip the firefighters with proper data to combat wildfires that broke out in the U.S. last year.
Several businesses have already used Artificial Intelligence and Machine Learning to their benefit, automated processes, improved security and are witnessing enhanced performance and efficiencies.
Factors influencing AI and machine learning adoption
Given the pace of innovations, Artificial Intelligence and its subsets, including Machine Learning, Big Data, Blockchain etc., are set to take over the world in the next decade. The factors that seem to be driving the fast-paced adoption of AI are:
- Next-Generation Computing Architecture: The CPUs will not be enough to manage the various facets of computing required for Artificial Intelligence and Machine Learning. Graphics Processing Units (GPUs) are needed to supplement the CPUs and are perceived to be key to AI. There is also a lot of development happening in the cloud infrastructure.
- Open Data: Open Source Software is said to be reason for the large-scale adoption and growth of various Machine Learning and Big Data products. Open Data is said to be straightforward and organizations don’t have to get into expensive contracts that can prove to be hard to get out of.
- Growth in Deep Neural Networks: Significant strides are being taken in deep learning, artificial neural networks, Transfer Learning and Capsule Neural Networks. These, experts aver, will change the way Machine Learning models are built and used.
- Historical Datasets: The development of cloud has made storage and accessing data easy. For Machine Learning models, access to huge datasets is crucial, as productivity and precision is directly liked to it.
Market, demand and growth numbers
The usage and development of Artificial Intelligence and Machine Learning is only going to increase further. Soon, we will have many more chatbots, digital assistants, virtual assistants and more automated vehicles.
It is estimated that the number of AI-powered voice assistants will touch 8 billion by 2023. Already, around 77% of devices we use have some or the other form of AI. Studies and reports have estimated the global AI market to be around $60 billion by 2025, while the global GDP is expected to grow by $15.7 trillion by 2030.
Emerging trends in AI and machine learning
AI will continue along the path to becoming the most transformative technology. Its impact is expected to be greater than we currently assume currently.
1. Augmenting workforce and jobs: With more jobs getting automated and outsourced to Artificial Intelligence, human resource will be free to take up more activities involving creativity, innovation and emotional intelligence. This is expected to impact workforce across sectors this year.
2. Bigger and better language modelling: Modelling allows machines to understand and communicate with humans. It can also take human language and convert it into computer code to run various applications and programs. Fast-paced development in this area by Open AI is expected to allow AI to take language and hold conversations; and with better computer codes, these conversations may even be indistinguishable from human conversations.
3. Cyber security: The World Economic Forum identified cybercrime as a more significant risk to society, compared to terrorism. AI is already being used to analyze network traffic, recognize patterns that seem out of place.
4. AI and the Metaverse: The Metaverse is a unified, persistent, digital environment where users can work and play together. Companies like Meta and Microsoft are helping build the Metaverse. According to experts, AI will be the lynchpin, helping create online environments for better immersive experiences.
5. Low code and no-code AI: Low code of no-code AI will democratize AI technology. For instance, one needed to know programming skills to create a website earlier; not so anymore. AI and ML are expected to follow the same growth trajectory.
6. Data-centric AI: The new idea is to focus on the data, as many applications beyond the big, mega tech companies have access to huge volumes of datasets. Unique datasets are structured. Better and higher quality of data that is labled; one can use domain expertise to lable the data and use AI and ML to develop better technologies.
7. Autonomous vehicles: Several players in the automobile sector are expected to announce major leaps forward. The year may see the first autonomous ship crossing the Atlantic.
8. Creative AI: AI has now become creative. AI can be used to design logo and info-graphics.
What this means for people wanting to join the domain
The interest in AI and ML is only likely to increase; while more and more organizations are all set to adopt AI functions. The adoption of these cutting-edge technologies are going to change how businesses operate and how people work.
Sustainable AI systems, given the current rate of innovation and adoption, will be able create over 38.2 million jobs across the world. What this means is that there is going to be a huge a demand for trained professionals, specializing in AI and ML soon.
Preparing for a career in AI and machine learning
Though Artificial Intelligence and Machine Learning have been around for a while, experts believe that they have only just scratched the surface of what AI and machine learning can do.
It is the right time for young professionals to equip themselves for a long and fruitful career with comprehensive AI and machine learning courses. These courses will help them learn everything there is to know about AI and machine learning, whilst also equipping them to take charge of innovations.
Hero Vired offers an Integrated Program in Data Science, Machine Learning and Artificial Intelligence. The program will equip one with skills to analyze data, build complex data models to solve business problems. By learning industry-leading tools and technologies, one can become a certified Data Science professional.
The syllabus will help candidates leverage big datasets and extract meaningful information from unstructured data. The syllabus will cover:
- Python and ML Foundations
- Foundations of Machine Learning
- Foundations of Statistics
- Working with Deep Learning Frameworks
- Application of Deep Learning in Computer Vision
- Application of Deep Learning in NLP
The Integrated Program in Data Science, Machine Learning and Artificial Intelligence will help candidates develop an in-depth understanding of Machine Learning methods, apart from building a strong foundation in mathematics and statistics.
These skills will help candidates take data-driven decisions. Candidates will also be able to earn transferable credits and eligibility for an MITx MicroMasters® program certificate, which is a pathway to Masters programs globally. Candidates will also get hands-on learning experience by working on real-world projects.