Artificial Intelligence, Big data, and machine learning are technologies that are at the forefront of digital transformation and technological revolution. I don’t know about you but I am curious about what the future of AI, machine learning and big data would look like? If you want to learn about these technologies in detail the join Intellipaat AI, Big Data, Machine Learning Training course today!
How would these technologies evolve in the years to come? How will it impact us? All these questions used to bother me but I have found answers to these questions and I will share it with you in this article. Can AI take your jobs? Can machine learning make machines smarter than humans or can big data help you make smarter decisions? We are already seeing shades of all this in some capacity.
Read More about, 5 Free Open Source Softwares for business
If you share the same interests as I do, then you are in the right place. In this article, you will learn about seven factors that will influence what the future of AI, machine learning and big data would look like.
-
Table of Contents
5G
With 5G on the horizon, users and businesses can reap the benefits of increased bandwidth and flexibility. This can prove to be a major breakthrough for edge computing. Even though businesses are struggling to decide on the right business models for 5G investments but once they decide on the right business model, they can see the real potential of 5G connectivity.
With blazing fast internet on the go, we will consume even more data and it will add to the big data revolution. Complex applications that require more bandwidth can work flawlessly with 5G, which was not possible in the past. Yes, 5G networks will take time to get into full effect but the future is bright for 5G.
-
Skills of Data Scientists
Another important factor that will affect how technology will evolve in the future would be the skills of data scientists. Cassie Kozyrkov, Cloud Chief for Google thinks that user experience of machine learning tools will improve significantly in the future. This means that these tools become less technical and more user-friendly. As a result, you might require less technical skills to harness the power of these tools. It would usually come down to how data scientists work across silos and still be more integrated in the business.
-
Data Security
Open source software has kick-started the big data and machine learning revolution by making it easy for us to create a machine learning and big data products and services. Unfortunately, most businesses tend to ignore the open data that they use for innovation. Irrespective of how good your algorithm might be, it will only deliver great results if you input high-quality data into it.
Chris Taggart, CEO of OpenCorporates, which is the biggest open database in the world shares his concerns and highlighted the issues businesses come across when they use proprietary datasets. According to him, there is some inconsistency as metadata is not shared across products and data provenance is not up to the mark.
Open data has its advantages too as it does not lock you into expensive contracts. Once you are stuck in one of these contracts, it is extremely difficult to get out of it. What businesses should do is to take care of open data just like they value their open source software and they can take advantage of the latest technologies. It is as important to protect your data stored in best dedicated servers as open data.
-
Growth of Internal Platforms
Businesses will harness the power of big data by putting it at the center of their product development and business process management. This will help them in driving innovation and come up with new and creative product ideas. Companies like BMW and Lyft are already working on this, as presentations from their data scientists suggest. Industrial companies or companies that focus on production and manufacturing will use data generating sensor which will be embedded in their products. Data coming through those sensors will help them take the right product decisions. All this will lead to a growth in the number of internal platforms.
-
Online-Offline Merger
The lines between online and offline will blur to the point that it will be hard to distinguish between the two. We are moving towards an online-offline merger and companies that understand this will succeed in the future. Companies like Amazon and Alibaba is already combining online and offline to deliver a unified shopping experience to their shoppers. They are creating physical store spaces on their website while their brick and mortar stores are still adapting to an online world. The ability to manage data at scale puts E-commerce giants like Amazon and Alibaba at the top of the E-commerce industry.
-
Real-Time Data
Companies who successfully build systems that are capable of capturing and managing real-time data will gain a competitive advantage in the future. With data backed decision making becoming more and more common in organizations, companies who capture, processes and extract insightful information from real-time data and take quick and accurate decisions fast will win the race. The ability to respond quickly to events will make all the difference. Amazon Web Services offerings help you achieve that with added scalability and features. Additionally, these tools are getting cheaper with each passing day.
find more: Machine learning course
-
Legal and Ethical Issues Will Rise
The surge in the number of IoT devices will also give rise to legal and ethical issues. With online privacy in a dismal state, we are seeing the enforcement of stringent laws to curb those legal and ethical issues. GDPR is a step in the right direction but there is a lot of things that need to be done to prevent these issues from growing. Companies not only collect a lot of data but they even sold it to the third party. In the future, they will have to clarify how they use customer data and why they are collecting their information.
Which factor would make the biggest impact on AI, machine learning and big data? Feel free to share it with us in the comments section below.
