There’s no doubt that artificial intelligence (AI) is a transformative technology – perhaps even the most transformative technology available today. But if you think the transformative nature of AI is limited to global tech giants and blue-chip companies, think again. AI is ultimately going to transform every business, in every industry.

That’s why every company needs an AI strategy.

Like any business transformation, if you want to get the most out of AI, it all starts with strategy. Your AI strategy will help you to focus on your core business objectives and prioritise ways that AI can help deliver those business goals.

In general, there are two ways businesses are using AI to drive success:

• Creating intelligent products and services
• Designing intelligent business processes

Let’s look at these two uses in a little more detail.

Intelligent products and services 

AI is, at heart, about making machines smarter, so that they can think and act like humans (or even better). We need only look at the popularity of devices like smartphones, smart fitness trackers and smart thermostats to see how consumers wholeheartedly embrace products and services that can make their life easier, smarter, more streamlined, more connected.

So it’s no wonder that businesses are increasingly looking for ways to make their products and services more intelligent through AI. Google’s search algorithms are an obvious example of an AI-driven tool. Amazon’s Alexa is another. Social media platforms also rely heavily on AI.

Chinese company ByteDance is, at the time of writing, the world’s most valuable startup. If you haven’t heard of them yet, you soon will. ByteDance product TikTok was one of the most downloaded apps of 2018. (If you’re wondering, it lets users create and share short videos.)

Another ByteDance product is Toutiao, which, thanks to its combination of the search engine, news and social media, is often referred to as ‘Buzzfeed with Brains’. Unlike Facebook and other social media platforms, Toutiao doesn’t generate news feed content for its users based on who they’re following; it uses AI to display a continuous stream of content that’s based on what the platform believes that user wants. In other words, it gets to know you as a user and recommends content based on what it believes you like and don’t like.

Setting aside these obviously techy examples, AI is also being used to produce smarter versions of far more traditional products. Vehicles, for example, are now much smarter than they were 10 years ago and can perform a range of tasks autonomously, from perfect parallel parking to alerting a driver who’s starting to nod off at the wheel. More and more vehicles can drive autonomously, as well.

Even Barbie has had a smart makeover. The Hello Barbie toy uses natural language processing and machine learning (both subsets of AI) to listen and respond to a child. Inside Barbie’s necklace is a microphone that records what the child says and transmits it to a server for analysis. Then, choosing from 8,000 dialogue options, the system chooses the most appropriate response for Barbie to say. All this happens in under a second.

What’s more, Barbie remembers useful information from conversations, such as the child’s favourite food or favourite pop star, to use in later conversations. In effect, Barbie learns what your child likes and dislikes, so she can hold more intelligent conversations. Creepy? A little, yes, to my mind. But it goes to show how even the most unexpected products are becoming smarter.

Intelligent business processes 

AI is being used to help businesses across all sectors optimise and automate business processes. This can be as simple as automatically recommending product B to every customer who bought product A, based on the preferences of other customers. Or it can be as complicated as fully automating an entire production line. For most companies, the biggest AI opportunities lie somewhere in the middle.

For example, credit reference agency Experian is using AI to crunch through its masses of data and make quicker, smarter decisions on credit scores and so on. American Express is doing a similar thing by using AI to detect fraudulent transactions in pretty much real time.

Predictive maintenance is another example of AI-optimised processes. This involves using sensors to constantly monitor vehicles and machinery to predict when parts might fail. (The idea being that, if you know when something is likely to fail, you can replace it beforehand and minimise downtime.) Volvo is using this technology to predict part failure and provide more accurate information on when vehicles need servicing.

Thanks to natural language processing and generation, machines can also be used to communicate with customers. In fact, customer service chatbots and messaging chatbots are now pretty much mainstream. But did you know that AI can also be used to generate longer, more specialist content? In the UK, the Press Association has partnered with news automation company Urbs Media to get robots writing thousands of news articles each month.

Finding the right AI use for your company

The right use for you will depend on what your business is trying to achieve. That’s why your AI strategy (here is an AI strategy template) must be driven by your overarching business strategy. So before embarking on an AI strategy, it’s vital you review your business strategy first.

Then, when you’re crystal clear on the business’s organisational goals, you can start to look at ways AI can help you achieve those objectives. To help you define your AI use cases for your business, using an AI use case template.

The post Why Every Company Needs An Artificial Intelligence (AI) Strategy For 2019 appeared first on Artificial Intelligence.

Machine learning is one of the hottest new trends in the IT industry right now, and it is all set to gain prominence in the future tech industry. With various companies like Microsoft, Apple, and Google rolling out their own developer tools, developer interest and engagement is at an all-time high. According to 96 percent of professionals, machine learning projects will rise dramatically in the near future, leading to job growth. This means you can apply for a lucrative machine learning job as long as you have the necessary skills. The question is, what qualifies as “necessary” when it comes to cementing a promising career and actually getting a machine learning job? While the scope of machine learning is vast, you can find success by honing a few specific skills — some basic and some not-so-basic. Let’s see what they are.

Always stay updated and relevant

Machine learning is considerably different from other avenues of technologies. How? Well, the growth rate of this technology is impressive. New methodologies, libraries, algorithms, techniques, and paradigms are frequently exploding onto the scene. For this reason, you must learn to stay current all the time. A good way to do so involves subscribing to the best tech blogs, reading research papers, and following technical conferences.

At the same time, keep in mind that the growth trajectory of machine learning is being influenced by industrial applications. So, depending on the type of projects you select and the effort you put into learning new tricks and tips of the trade, you can manage the demands and requirements of this industry. This will help develop a well-paid, fulfilling, and growth-focused career in the field of machine learning.

Learn the prominent machine learning languages

You will not be able to hold onto an ML job if you lack knowledge in these languages. Each of them serves a specific purpose. C++ is useful for speeding up any coding work you might have on your plate, while R works wonders when it comes to plots and statistics. Hadoop is a Java-based programming language, which means you’ll have to implement reducers and mappers in Java.

Brush up on your statistics and probability skills

The more theories you learn, the easier it gets to understand algorithms. But understanding Hidden Markov models, Naive Bayes, and Gaussian Mixture Models is difficult without a thorough understanding of statistics and probability. You can use statistics in the form of a model evaluation metric for p-values, receiver-operator curves, and confusion matrices.

Possess great programming skills

To be in demand for a hot machine learning job, you must learn everything you can from the world of programming. Brush up on your programming and computer science knowledge because getting a machine learning job requires you to be totally comfortable with concepts like algorithms, data structures, and computer architecture. Bear in mind that ML algorithms do not function in isolation and are mostly a part of bigger systems. So, ML programmers must get comfortable working with APIs to create future-ready interfaces. You will also find it useful for learning about the fundamentals of the software development life cycle.

Perform data modeling and evaluation

You must continually evaluate the efficiency of a specific model. Based on the tasks you have at hand, you must select suitable error measure and accuracy, and implement a suitable evaluation strategy.

Understand distributed computing

In more cases than one, a machine learning job involves working day in and out with huge volumes of data. Right now, it is impossible to process this data with a single machine; the right way would be to distribute it throughout a whole cluster. Projects like Apache Hadoop and cloud services such as EC2 from Amazon simplify the process and even make it a lot more cost-effective.

Be aware of signal processing

When you are part of an enterprise, you may get the chance to work with signal processingprofessionals who belong to the ML and data science teams. However, it’s a good idea to learn some of the fundamental concepts and ideas from this body of knowledge yourself. Focus on features extraction to further your machine learning career, and the best way to do that is signal processing.

Related algorithms allow you to resolve problems in various innovative methods using algorithms like curvelets, wavelet, bandlets, and contourlets. Another significant signal processing technique is Fourier analysis and convolution that provides great benefits to machine learning professionals. However, keep in mind that signal processing is not always easy to learn. But once you do achieve this milestone, you will become an unstoppable force in the field of machine learning.

Become an expert in UNIX tools

You should brush up your skills in UNIX tools like grep, awk, cat, cut, sort, sed, tr, find, and others. Because all the processing is performed on a Linux-based machine, access to such tools is a must. Become familiar with their functions and learn to utilize them carefully. UNIX tools can make your life easier and raise the odds you’ll be considered for a machine learning job.

Fuel your sense of curiosity

If you wish to make a name for yourself in the machine learning sector, you need to find sustenance and success. And that is possible only if you possess an unbeatable sense of curiosity. You should always be willing to learn more and keep an open mind when it comes to new concepts in the field of machine learning.

Get in on the boom with a machine learning job

Machine learning is a booming industry right now, and a lot of people are trying to get in on the ground floor of this rising trend. But how can you develop your skills? Well, you need to start with a strong quantitative background. Without that, charting a career path in ML can be difficult.

But if this is something you are passionate about and want to make this your lifelong goal, you should not allow your background to discourage you from pursuing your career ambitions. There are lots of courses available that have been endorsed by the industry and provide you the opportunity to learn all that you need to succeed in machine learning.

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