AI programming is an elevation of technology that has brought efficiency and optimum benefits to different company’s operations and people’s lives. AI has brought another level of smart technology to different industries and the prospects of its potential still grow with the expectation that it would reach human intelligence. This is because developers are willing to explore, experiment, and implement their capabilities to satisfy more of the human and organization necessities. After all, necessity is the mother of invention.

Therefore, here is the list of top 5 programming languages that are in trend for AI developments in 2020.

Python

Python is an Interpreted language which in lay man’s terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.

Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.

Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.

R

R is a multi-paradigm language that can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualization, and statistics in general. However, it is less flexible compared to Python but is more specialized too.

R is an open-source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

Java

One of the best things about Java is Java Virtual Machine Technology. This technology allows developers to build a single app version that will run on all Java-enabled computing platforms. Major strengths of this programming language are as following: maintainability; portability; transparency.

AI is closely connected with search algorithms, genetic programming, and the use of artificial neural networks. Java in the artificial intelligence sphere may be more than useful. Programming AI in Java has many benefits: easy use, debugging ease, simplified work with large-scale projects, facilitated visualization, better user interaction. Another reason for programming AI in Java is the incorporation of Swing and SWT (the Standard Widget Toolkit). These features make graphics and interfaces look appealing and sophisticated.

Another reason for using Java in AI programming is the vast amount of tutorials on the Internet. Just type “how to program artificial intelligence in Java” and you’ll get a lot of pages to choose from. Java is versatile. It’s used for making multi-robot systems, sensor networks, and machine learning suites.

Scala

Just like Java, Scala belongs to the JVM family. Scala is a fairly new language in the AI space but it’s finding quite a bit of recognition recently in many corporations and startups.

It has a lot to offer in terms of convenience which is why developers enjoy working with it. Also, ScalaNLP, DeepLearning4j, etc are all tools and libraries that make the AI development process a bit easier with Scala. It’s good for projects that need scalability and combines the strengths of Functional and Imperative programming models to act as a powerful tool that helps build highly concurrent applications while reaping the benefits of an OO approach at the same time.

Scala provides good concurrency support which helps with projects involving real-time parallelized analytics. It has a good open source community when it comes to statistical learning, information theory, and Artificial Intelligence in general.

Rust

The most loved language of all is Rust, an open-source programming language that was hatched by tech pioneers at Mozilla in 2010.

In fact, Rust has been voted the most-loved language for the past four years in Stack Overflow’s annual developer surveys as solves pain points present in many other languages, providing a solid step forward with a limited number of downsides.

Rust concepts are also being used in Microsoft’s recently open-sourced Project Verona, an experimental language for safe infrastructure programming that could help Microsoft securely retain legacy C and C# code.
Mozilla Research describes Rust as a “systems programming language that focuses on speed, memory safety, and parallelism”.

The post TOP PROGRAMMING LANGUAGES IN TREND FOR AI PROJECTS IN 2020 appeared first on Artificial Intelligence.

The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.

In looking back on the early stages of the VA’s newly launched artificial intelligence program, the department’s Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.

“One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges,” he said at AFCEA’s Automation Transformation conference.

Some of the most pressing concerns the VA’s AI program has encountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.

Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.

“There’s a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related,” he said. “Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where it’s located. Having all that put together makes for an efficient use case and allows us to better work together.”

Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone. 

“When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you don’t get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them,” he said.

Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.

The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.

“How do you make data available considering privacy and other concerns?” Alterovitz said. “One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but can’t be identified because it generates at the individual level a completely different data set that has similar statistics.”

Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, “You can take the data, and then vary that information so that it’s not the exact same information you received, but is maybe 20% different. This makes it so you can show it’s statistically not possible to identify that given patient with confidence.”

Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.

“A lot of the data we have wasn’t originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues,” Alterovitz concluded

The post VA Looking to Expand Usage of Artificial Intelligence Data appeared first on Artificial Intelligence.

The first question you need to consider is: what are the specific business drivers for your AI project?

In a broader context, the likelihood is that you are driven by a desire to gain or sustain a competitive advantage or to enter into new areas of business, stave off competitors, etc. Those are important drivers, but if everyone is getting into AI then they could arguably be table stakes. AI is a long-term investment and you need to think beyond the short term. Assume that your competitors and future competitors in adjacent markets are also at least considering the use of AI to keep parity. Reducing costs is a key driver – but just as important and just as overlooked is the fact that in the not too distant future both your suppliers and your customers will be expecting you to leverage AI. You need to think holistically about the short and long-term drivers, both for the internal and external factors that affect your business…

Unexpected Impact

The impact of AI on business processes gives us some cause for concern… Many seem to think this impact will come further down the line. In reality, even a small change in a process can have a big impact on a worker’s or department’s daily life. It depends on how you define a big impact. AI can have an immediate impact on the day-to-day work of individuals but it may take time for the results of all those small changes to have a big impact on the business as a whole. You need to plan for an immediate impact on your processes, as well as the greater impact AI will have in the mid and long term. Your processes will change, and hiccups and push backs not identified or mitigated against at the start could derail your project.

Finally, let’s consider your existing business operations; for example, your sales, HR, procurement, accounts, marketing processes, etc. To you these may seem to be straightforward and discreet activities. An AI system may not view these things as so clear cut; it may see more optimal ways of working, it may not respect the cultural silos that exist within your organization. Outside of very narrow, niche projects, the use of AI will likely have a knock-on impact, including potentially unexpected impacts, across your organization. Remember, when we said that you need to think holistically when planning the use of AI? Well, consider the chain effect of automating decision-making in one aspect of your business and how that may affect other areas of your business.

You must ask three essential questions:

1. What is the project? What value will it bring? What impact will it have?

2. What will the project involve in terms of changes to existing systems, processes and the organization?

3. What financial benefits will the project bring?

Costs to consider

Like IT projects, staff costs are a significant chunk of the overall costs. Because of a talent shortage in certain areas, the salaries for AI specialists can be higher than other IT roles. Also seemingly basic tasks like labeling of the training data can be a significant cost.

In AI projects, you also will have to budget for data and computing environment costs. Plus, if your teams need any external consulting support for opportunity identification, roadmap development, and working alongside the team, that cost also has to be considered. In contrast to normal Central Processing Units (CPU) in our computers and laptops, Graphical Processing Units (GPU) are special purpose hardware chips that are needed to optimize many machine learning applications. Another special purpose hardware chip is the Tensor Processing Unit (TPU) which is optimized for certain machine learning applications.

GPUs/ TPUs can make your machine learning applications run faster, but keep in mind that they can be more expensive compared to normal CPU-based environments…

The likelihood is your AI project is going to be processing a lot of data, fast. You may decide to use optimized on-premise servers or utilize cloud services such as Amazon, Microsoft or Google. Either way, you need to budget carefully. We suggest you do a five-year cost analysis as low monthly fees can stack up versus high initial costs for on premise hardware.

Finally, most AI projects will use some kind of external consulting and this can also be expensive. However, there can be great value in engaging experts that have AI project experience, saving you from reinventing the wheel.

This is the budget in the initial phases when you are about to begin your AI journey. After that, you will need to factor in solution deployment, solution management and solution retraining costs.

AI projects may require extra governance and specialist oversight teams. In fact, AI projects will be staffed differently to traditional IT projects and the vendor partnerships you will form will have deeper and different dimensions to them.

And, of course, AI projects will require a significant amount of change versus the status quo. Change may well be profound and any change, no matter how small, needs to be managed.

The post A roadmap to using Artificial Intelligence appeared first on Artificial Intelligence.