Machine learning (ML) is the process that allows a computer to execute something that it has not been specifically told to do. Therefore, ML takes on the central role in making smart machines a reality. With Sophia’s release, an AI robot built by Hanson robotics, we wonder how close we are to be surpassed by these smart fellows.
An inherent note of warning in various industry surveys on AI and its influence on industries is that software merchants should initially focus on understanding the business-customer requirements and potential business benefits from AI. Before pursuing the gold rush, referred to as AI Washing, as recommended in How Enterprise Software Providers Should (and Should Not) Exploit the AI Disruption.
The trust shortfall in the abilities of tech-enabled solutions that currently exist will disappear in the next decade, stated In Ten Years: The Future of AI and ML. Over the next decade, we will experience a fundamental change from partial mistrust and skepticism to complete reliance on AI and other advanced technologies.
Most AI-powered applications are consumer-facing, which is another major reason for usual users to win the trust barrier over time. With extra exposure and extra access to technological solutions for their daily business, the Citizen Data Science community will set a new-technology-order world stage.
Predictions About Machine Learning
An experienced user of ML techniques shares his views into the world of ML, recommending that the following trends are inevitable in the field of ML:
Use of Multiple Technologies in ML: The rising of IoT has helped Machine Learning in several ways. The use of numerous technological strategies to accomplish improved learning presently practices in ML; more “collaborative learning” by using numerous technologies is likely to happen.
Personalized Computing Environment: Developers will have access to API kits to create and release more intelligent applications. To an extent, this effort is similar to “assisted programming.” Through these API kits, developers will comfortably embed facial, speech, or vision-recognition features into their systems.
Quantum Computing will nobly improve the speed of execution of ML algorithms in high-dimensional vector processing. This will be the next victory in the sector of ML research.
Future advancement in unsupervised ML algorithms will cause higher business results.
Tuned Recommendation Engines: ML-enabled services of the future will become more correct and significant. For instance, the Recommendation Engines of the future will be greatly relevant and very close to an individual user’s personal preferences and tastes.
Future of Machine Learning
1. Fine-Tuned Personalization (Ben Wald, Co-Founder & VP of Solutions Implementation at Very)
Machine learning might be a data analysis method, although it’s regularly influencing the lives of those who possess IoT devices like smartwatches, phones, cars, and more. This is what Ben had to say about the unusual relationship between machine learning and consumers.
With 90 percent of every data generated over the previous two years, much of it increases from a collection of smart devices that connect our phones, wrists, and homes. Thus, companies have better ways to develop relationships with their customers.
By implementing machine learning, corporations can fine-tune their target audience’s understanding to inform product development, marketing, and sales. With algorithms to simplify precisely how their products are being used, developers and designers can customize products more than ever before, enhancing the value for both the company and the consumer.
With more success in machine learning algorithms, we will start to see hyper-targeting and fine-tuned personalization for customers on a bigger scale.
2. Better Search Engine Experiences (Dorit Zilbershot, Chief Product Officer at Attivio)
You may likely not be conscious of it when scrolling through Google searching for an article, but the ranking of those results is done for a reason. Recently, machine learning has had a massive influence on search engine results. This is what Dorit has to say about it.
“Search engines will enhance both the user and the admin experience by significantly over the next few years. With additional development of neural networks and deep learning, search engines of the future will be much better at delivering answers and highly relevant views to the user’s search.
We’re currently extremely good at understanding what results should be served based on the users profile and the query. Although, this process still needs manual configurations and understanding of how search engines work. Results will be suited close to the individual based on their past interactions, preferences, and the used words without any manual administration.
We will also get proactive about alerting people on potential difficulties before they occur and offer actionable suggestions to ensure a smooth operation and interesting search experience.
With lots of content being published every second of the day, it will be exciting to see the techniques machine learning algorithms continue to optimize search results with the user in mind.
3. Evolution of Data Teams (Henrique Senra, VP of Product Development at SlicingDice)
It is not rare for IT and data teams to be delayed with programming and systematic tasks. Although Henrique believes more improvements in machine learning will help evolve the day-to-day of these teams.
It’s almost impossible to foretell the future of ML and AI. If you told technology professionals two decades ago what we could do with ML today, they would likely be doubtful.
There are certain trends in how ML is being implemented presently and how those cases will develop in the future. ML will be one of the foundational tools for advancing and maintaining digital applications in the future. This means that IT/data teams will spend little time programming and updating applications, but instead have them learn and keep enhancing their operations continually.
Additionally, more intelligent robotic process automation with machine learning assistance will decrease the number of redundant tasks executed by programmers.
4. No-code Environments (Tony Fader, ML/NLP Software Developer at AppSheet)
Machine learning is probably going to advance data teams’ tasks, but it’ll also be more approachable for a bigger range of audiences. This is what Tony says about this recent phenomenon.
Machine learning will become another part of software engineering. Open-source frameworks such as Tensorflow, Keras, and PyTorch have standardized the way people utilize machine learning algorithms and dismissed the requirements for doing so. Ph.D. isn’t a requirement to do machine learning.
You only need to download some relevant packages and follow an online course to get up to speed. Most companies are taking it a step further and allow anyone (not just programmers) to use no-code machine learning in their customized apps.
It may sound like utopia, but with lots of infrastructures, datasets, and tools available presently, these types of environments are gradually but certainly rolling out.
Advantages of Machine Learning
1. Supplementing Data Mining
Data mining is the process of observing a database—also, various databases to process or analyze data and generate information.
Data mining means to find properties of datasets. At the same time, Machine Learning is about learning from and making predictions on the data.
2. Automation of Tasks
It is the development of autonomous computers, software programs. Autonomous driving technologies, face recognition are other examples of automated tasks.
In the next decade, AI applications will become more routine than ever. Thus, every service provider will need to effectively upgrade their hardware (storage, backup, computation power, etc.) and software (servers, networks, ad-hoc networks, etc.) abilities.
The world of one trillion IoT devices we expect by 2035 will deliver infrastructural and architectural issues on a new scale.
Our technology must keep advancing to withstand. On the edge computing side, it implies that Arm will continue to greatly invest in developing the hardware, software, and tools to allow intelligent decision-making at every point in the infrastructure stack.
It also means using heterogeneous [computation] at the processor level and through the network from cloud to edge to an endpoint device.”
When we look at history and where we are currently, it looks like the evolution of edge machine learning is swift and unstoppable. As future developments continue to happen, prepare for a strong influence, and ensure you’re ready to grab the opportunities this technology brings.