How Influential Businesses like Google Are Using Artificial Intelligence and What We Can Learn from Them

AI is a hot buzzword right now and businesses of all shapes and sizes are trying to find a way to leverage its power for their own particular needs. Exciting, futuristic, and vaguely science-fictiony, AI is currently seen as a must-have tool for turbo-charging one’s business and improving the quality of customer experience. It’s been a game changer for enterprise companies like Google, Microsoft, Amazon, and IBM who have the computing power and resources to access field experts. Small companies often aren’t in the same ball game, yet there remains the need to get in the action if they are going to keep up with the times. Fortunately, cloud computing and open-source machine learning APIs are bridging the technical gap for smaller companies, making it possible for them to take advantage of the endless possibilities offered by AI. More than ever, this is the time for small businesses to jump in and start experimenting. The real challenge, however, is knowing how to utilize these new resources well. Here again, small businesses can look to larger enterprise companies, such as Google, to gain insight and build on their ideas. Here’s what we can learn.

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Arcbees Acquires Chalifour Digital Solutions

NEWS

Québec, October 2, 2017– Arcbees, a leading technology company in artificial intelligence, announces the strategic acquisition of Chalifour Digital Solutions. This transaction will broaden the company’s areas of expertise, focus joint development efforts and strengthen its market positioning in intelligent business solutions.

“There is no better alliance to continue the development of Prehos, a multi-faceted digital solution committed to making life easier for prehospital workers and improving patient care. Arcbees was the clear choice to pursue this project that I have been working on for a while and our collaboration will benefit the mandates that Prehos currently holds here and elsewhere in Canada, “says Christian Chalifour, happy with this new alliance.

“This is a promising transaction that makes sense in the complementarity of our services. The combination of our strengths had become natural. With artificial intelligence, we can automate processes, predict results, suggest actions to be taken, classify, optimize, detect errors, analyze images, texts, and more. Préhos, is one of the projects that confirm our combined expertise,” says Christian Goudreau, president of Arcbees, specifying that the transaction will allow Christian Chalifour to join Arcbees as a strategic consultant in IT solutions and marketing, while remaining president and co-founder of Préhos.

Arcbees is already working with major clients such as Québec City’s Jean-Lesage International Airport, Kronos Technologies, Optel Vision, Familiprix, and WikiNet, and intends to pursue its development in the field of artificial intelligence-based technology solutions, worldwide.

ABOUT ARCBEES.

Founded in 2010, Arcbees quickly became one of the most inventive companies in terms of digital business solutions. The creation of an open source tool that allows developers to build business applications more efficiently has enabled Arcbees to be recognized as an international player. Today, the company, based in Quebec City, has 30 employees and is considered as a strategic and technological partner for all companies wishing to enhance their operational efforts by combining web, mobile and software solutions to their enterprise data.

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SOURCE:

Arcbees | www.arcbees.com

FOR INTERVIEWS:

Anne-Marie-A. Savoy | annemarie@fernandezcom.ca |

T 418 704-1559, ext. 228 | C 418 934-7448

FOR INFORMATION:
Charles-Antoine Richard | carichard@arcbees.com |
1 844 527-2233, poste 703 | 581-982-3957

3 AI Fails and Why They Happened

In little over a decade, AI has made leaps and bounds. Every day, new headlines showcase the most recent advancement in AI. In fact, advancements are accelerating:

  • 2004 DARPA sponsors a driverless car grand challenge. Technology developed by the participants eventually allows Google to develop a driverless automobile and modify existing transportation laws;
  • 2005 Honda’s ASIMO humanoid robot is able to walk as fast as a human, delivering trays to customers in a restaurant setting. The same technology is now used in military robots;
  • 2007 Computers learned to play a perfect game of checkers, and in the process opened the door for algorithms capable of searching vast databases of information;
  • 2011 IBM’s Watson wins Jeopardy against top human champions. It is currently training to provide medical advice to doctors. It is capable of mastering any domain of knowledge;
  • 2012 Google releases its Knowledge Graph, a semantic search knowledge base, likely to be the first step toward true artificial intelligence;
  • 2013 Facebook releases Graph Search, a semantic search engine with intimate knowledge about Facebook’s users, essentially making it impossible for us to hide anything from the intelligent algorithms;
  • 2013 BRAIN initiative aimed at reverse engineering the human brain receives 3 billion US dollars in funding by the White House, following an earlier billion euro European initiative to accomplish the same;
  • 2014 Chatbot convinced 33% of the judges that it was human and by doing so passed a restricted version of a Turing Test;
  • 2015 Single piece of general software learns to outperform human players in dozens of Atari video games;
  • 2016 Go playing deep neural network beats world champion.

Source: Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures, https://arxiv.org/pdf/1610.07997.pdf

However, little information is shared on the failures in AI and even less on why they fail.

Failure is king

Failure is at the core of human advancement. For example, the microwave’s invention was a failed attempt at making a military grade radar during WW2. Percy Spencer noticed a melting chocolate bar in his pocket while working on magnetrons for Raytheon, a major U.S. defense contractor.

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Data and AI: The Reason Why They Can’t Live Apart

In this article, we will discuss the importance of data and their analysis, in order to design efficient and effective artificial intelligence (AI). But first of all, let’s set up an example to help us understand.

A typical problem to solve for a company is to anticipate the needs of their customers. For example, an insurer wants to know which insurance coverage would be most suitable for his client. Several factors can consciously or unconsciously influence the client’s choice of insurance (and also insurer!): Age, education, health, current and future financial situation, short and long-term objectives and so on! With the ability to serve a wide range of clients, it can become difficult for the insurer to have in mind all possible customer scenarios and recommend the appropriate product. Consequently, an AI could be conceived to handle this kind of situation. Assisting the insurer, it could quickly identify the type of client and suggest which insurance and options would suit him best.

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A Guide to Working Remotely, as a Team.

How half our team traveled abroad for two weeks and still stayed productive.


We’re no strangers to working remote.

Back in October, I worked from Banff, Alberta because I am passionate about climbing mountains. (Summits are awesome!)

Our Branding Goddess and Design Adventures, Manon, spent 4 months working remotely while traveling the world last year. She wrote all about it in this great guide.

#AwesomeJobTitles

This year, we pushed the concept further. Eight of us moved to Puerto Rico for two weeks and still managed to produce great results. Here is how we did it and what we learned.

Continue reading “A Guide to Working Remotely, as a Team.”

An Introduction to SVG – Part 3

Hey guys, welcome to another post about SVG. So far we’ve seen how to create, stylize, and load SVG shapes and we took a look at the SVG coordinate systems, the viewport and the viewbox. By now, you have acquired some basic knowledge on SVG and you might have ideas or little tricks of your own.

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Introduction to Recommender Systems

Many receive advice, only the wise profit from it.” – Harper Lee

Many of us see recommender systems as mysterious entities that seem to know our thoughts. Just think of Netflix’s recommendation engine which suggests us movies or Amazon which suggests what products we should buy. Since their inception, these tools have been improved and refined to continuously improve user experience. Although many of them are very complex systems, the fundamental idea behind them remains very simple.

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2016: When life gives you lemons…

We have a confession to make: At Arcbees, we liked 2016.

Yes, you read correctly. Unlike many others who want 2016 to quickly fade away in the meanders of oblivion, for us, it was an inspiring year.

Here are our 3 reasons why we loved it and why we will surely love 2017.

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A Non-Technical Guide To Understanding Machine Learning

In last week’s post, we discussed if machine learning was right for your business. As part of that effort, I recently went through the process of learning the ins-and-outs of machine learning and realized most information out there is technical and aimed at developers or data scientists.

I thought an explanation from a non-technical person might be of interest.

Let’s begin.

What exactly is machine learning?

The simplest definition I came across:

Machine learning is “[…] the branch of AI that explores ways to get computers to improve their performance based on experience”. Source: Berkeley

Let’s break that down to set some foundations on which to build our machine learning knowledge.

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Is Machine Learning Right For Your Business?

Most businesses recognize that machine learning can generate exceptional value but many still wonder how, in what specific areas, and if the time is right to integrate it within their data strategy. Today, we explore what questions you should be asking to know if machine learning is right for your business. Continue reading “Is Machine Learning Right For Your Business?”