Artificial Intelligence Vs Machine Learning Vs Software Engineering

If you want to know what is better to study Artificial Intelligence or Machine Learning or Software Engineering and want a comparison between Artificial Intelligence, Machine Learning and  Software Engineering then you are at the right place, as we are going to cover all these points and you will get a clear idea about these fields.

A great article claims that the best way to understand the state of technology is to look for differences between the different strategies. It’s called software engineering vs AI vs Machine learning.

It contains a lot of points about where you can and cannot use AI, Machine learning, and Software engineering in your business. We have aggregated some of those above points into a list below but in the end, those points are just as good when they’re all together. You can filter all these points together on our website or even see more of them up in the comments.

It should be clear that we have no objection to any of these forms of innovation.

The overall list makes for good bread and butter reading as well as those points where we see ourselves taking a different stance from the whole debate.

 

Artificial Intelligence:

AI systems today are small data crunchers but their power is growing rapidly. They can emulate parts of human speech, tell who has said a particular thing or what someone means.

One of the most useful examples of AI in business is the way that IBM uses its Watson supercomputer for various business processes. Watson has been deployed in dozens of companies to remove unnecessary labor and explore new opportunities that previously would have taken years to understand or access data.

Machine learning: 

Machine learning brings machines into the software/hardware / IoT / AI / etc. Machine learning can solve, or be used to solve all kinds of problems. 

For example:

Machine learning software can replace humans who have bias biases for example: including ageist biases, for which robots will likely not have a bias in their workplaces.

Influencers don’t seem interested in if machines can outperform them — to them, machines simply perform simple tasks with a similar but increasingly sophisticated approach. We have ways to investigate what would/could happen without falling into the same pitfalls that AI systems fall into.

Machine learning applications use AI systems to build models which then have the intelligence to refine those models so that machines have high levels of practical value. AI systems are software and may have low-level development requirements. Many would have to learn and update with time.

The amount of data possible to train a machine learning algorithm — as well as endless computing power needed to run the algorithms — means machine learning can apply to core verticals and industries as diverse as health care, energy, security, and more.

Software engineering: 

Software engineering brings your software engineers back to training the computers of today and hopefully tomorrow.

Meanwhile, we have some large well-funded startups that are not working on intelligent applications for your business at the moment. While we applaud these VCs for giving their engineers new jobs in smart AI, those engineers will likely end up as a herd of political scientists working on data-driven policies in the likes of Washington, D.C.

As we all know, startups are trying to break the mold by moving away from the strict product mindset. This mindset stands in the way of proper software engineering.

In theory, there are many opportunities to create smarter robots in general: automation, simplification, and automation of “bad” tasks. Most of these opportunities (and many likely companies) are best served by enough software engineers to develop better apps and algorithms and also enough AI developers who can help those app developers develop better algorithms for their new jobs.

Software engineers will still be important as long as people want to conduct every business activity. We hope that AI will help human beings better perform certain tasks — and that AI (or artificial intelligence) software engineers will be able to do even better tasks than they’re currently doing and help make those tasks easier to accomplish.

System design: 

System design -- what does system can be designed, and what should be designed?

Systems will continue to evolve in these areas, and in the process, they’ll change significantly. Systems will have complex and distributed uses, more intelligence in the hands of some humans, and many interactions with other third parties. How much we rely on automation in new applications will depend on changes in the business world as a whole and whether the expectations of consumers and/or politicians change.

Likewise, how software developers create “compute” apps with enough computing power in their hands will go a long way towards where we’ll all be interacting with AI.

How AI is currently created — by programmers building programs with programmable objects of different sizes and/or operations that can control some physical parts of the system — will evolve to include a whole new class of software developers and a powerful new way of thinking. They’ll have a hand in how different smart systems function and interact.

Theoretically, it should be obvious that the programs that AI systems make should change with time. 

Conclusion:

As all fields are best at their place important is in which field do you have an interest. One thing is clear that the future in these fields will be bright. It may sound worrying that this stream will reduce many job opportunities, but at the same time, it will also produce at least an equal number of job opportunities.