Machine Learning and Artificial Intelligence

Artificial Intelligence sounds like a futuristic world where robots are going to take over. The reality is the future is now and we have the ability to make use of AI in our every day lives and in our work in particular. AI is a broad concept covering machines that have the ability to complete processes that we would consider smart. Machine Learning takes this a step further in that it entails machines accessing data and learning for themselves rather than being programmed to understand and/or complete tasks.

Using IBM’s Watson Analytics it is possible to ask a natural language question and get answers without having to program the system to come up with that specific answer. The system then learns what is more appropriate and helpful as answers are selected and the picture of the information is refined. Future questions are answered faster and even more appropriately. This really is the smarter future where information is reported on as seamlessly as new movies are recommended within Netflix. In fact we are already so entrenched in the future of Artificial Intelligence that it seems common place. We even think it’s a pain to shop on a site that doesn’t recommend a better alternative when we’re trying to find that specific item we need. We expect the website to use AI and ML to suggest what other customers have bought or similar items that we might be interested in.

Over the past 5-10 years it has become a common question – why can’t I get ‘my report’, ‘my information’, ‘the answer’ (you fill in the blank) not only as quickly as Google but with a similar understanding and reasoning of the question as Google. Now with systems like Watson Analytics it is possible to ask a question in your own words and get an answer that has reasoning and logic built into it – a truly smart answer.

While many machine learning calculations have been around for quite a while, the capacity to consequently apply complex numerical counts to enormous information – again and again, quicker and quicker – is a current improvement. Here is a couple of broadly announced cases of machine learning applications you might be acquainted with:

  • The intensely advertised, self-driving Google auto? The path of machine learning.
  • Online suggestion offers, for example, those from Amazon and Netflix? Machine learning applications for regular day to day existence.
  • Knowing what clients are saying in regards to you on Twitter? Machine learning combined with a real understanding of words and their meanings.

Why is machine learning vital?

Resurging enthusiasm for machine learning is because of similar variables that have made information mining and Bayesian examination more famous than any other time in recent memory. Things like developing volumes and assortments of accessible information, computational handling that is less expensive and all the more capable, and moderate information stockpiling.

These things mean it’s conceivable to rapidly and consequently create models that can break down greater, more unpredictable information and convey speedier, more precise outcomes – even on an extensive scale. What’s more, by building exact models, an association has a superior possibility of recognizing beneficial open doors – or evading obscure dangers.

Oil and gas

Finding new well locations. Breaking down minerals in the ground. Anticipating refinery sensor disappointment. Streamlining oil appropriation to make it more proficient and practical. The quantity of machine learning use cases for this industry is tremendous – and as yet growing.


Breaking down information to distinguish examples and patterns is critical to the transportation business, which depends on making courses more productive and anticipating potential issues to build benefit. The information investigation and displaying parts of machine learning are imperative devices to conveyance organizations, open transportation and other transportation associations.

Social insurance

Machine learning is a quickly developing pattern in the medicinal services industry, on account of the coming of wearable gadgets and sensors that can utilize information to survey a patient’s wellbeing progressively. The innovation can likewise enable restorative specialists to break down information to recognize patterns or warnings that may prompt enhanced analyses and treatment.

Showcasing and deals

Sites suggesting things you may like in light of past buys are utilizing machine figuring out how to dissect your purchasing history – and advance different things you’d be occupied with. This capacity to catch information, dissect it and utilize it to customize a shopping background (or actualize a promoting effort) is the fate of retail.

Budgetary administrations

Banks and different organizations in the money related industry utilize machine learning innovation for two key purposes: to distinguish critical experiences in information, and avoid misrepresentation. The experiences can distinguish speculation openings, or enable speculators to know when to exchange. Information mining can likewise distinguish customers with high-hazard profiles, or utilize cyber surveillance to pinpoint cautioning indications of extortion.
For customers of ours in Calgary, Edmonton, Saskatchewan and British Columbia we’ve been using advanced analytics to boost profit, reduce costs, and save time. Send us an email at or call at (587) 885-1090 to request your FREE Watson Analytics access and like thousand of others, give Watson analytics a test-drive! 

Leave a Reply

Your email address will not be published. Required fields are marked *