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CRN Fast50 Award

CRN Fast50 for 2019 Award

CRN Fast50 - Number 15TechConnect was listed in the CRN Fast50 for 2019, settling into the 15th position in our debut year. A day after receiving the Deloitte’s Technology Fast 50 awards we were again presented a great result. Read about the Deloitte’s Technology Fast 50 here if you missed it.

The CRN Fast50 award, now in its 11th year, recognises the fastest-growing companies in the Australian IT channel, based on year-on-year revenue growth. “The 2019 CRN Fast50 put up astounding numbers: they grew at least 15 times faster than Australia’s economy.” – Simon Sharwood, Editorial Director – CRN. CRN Fast50 Award

This was the first year that TechConnect has entered this award and are thrilled to be listed 15th, with a growth rate of 67%. Simon Sharwood also stated, “It’s a huge achievement to have made it into the CRN Fast50. Your company’s growth not only outpaced most others in our industry, it also vastly exceeds Australia’s current overall growth rate!”.

This is a great achievement for us, we would like to thank our customers and team, without them this would not have been possible. Team TechConnect would also like to extend a congratulations to all the other winners. TechConnect looks forward to expanding our growth and moving up the list in coming years.CRN Fast50 - Clinton Thomson

Deloittes Technology Fast50

Deloitte – Technology Fast 50 Australia 2019

Deloittes Fast50 AwardWe are extremely excited and honoured to have been listed as one of Deloitte’s Technology Fast 50 Australian companies. Ranking 43rd on the list with a growth rate of 161%, in our debut year. This is a huge achievement not only for the company but also for our team and the hard work they have put in to provide solutions for our customers.

“Now in its 18th year in Australia, the Deloitte Technology Fast 50 program ranks fast growing technology companies, public or private, based on percentage revenue growth over three years.” – DeloitteDeloittes Technology Fast50

“More than ever, this year showcases world-leading Australian business innovation and it is a tremendous achievement for any company to be named among the Deloitte Technology Fast 50,” Deloitte Private Partner and Technology Fast 50 Leader, Josh Tanchel.

With our significant growth over the years, our hard-working team and most importantly our valued customers, we were able to achieve this amazing outcome. TechConnect is getting ready for exponential growth, through expanding into new markets and deep data specialisations.

The awards night was held on Wednesday, 20th of November in Sydney at the Museum of Contemporary Art. Clinton, our Director, was there to accept the award on behalf of TechConnect. “It was a great night; Well done to all the other recipients and thank you to Deloitte’s for putting it all together.” Clinton Thomson, TechConnect IT Solutions.Deloitte Fast50 - Clinton Thomson

What's the difference between Artificial Intelligence (AI) & Machine Learning (ML)?

What’s the difference between Artificial Intelligence (AI) & Machine Learning (ML)?

What’s the difference between Artificial Intelligence (AI) & Machine Learning (ML)?

The field of Artificial Intelligence encompasses all efforts at imbuing computational devices with capabilities that have traditionally been viewed as requiring human-level intelligence. 

This includes:

  • Chess, go and generalised game playing 
  • Planning and goal-directed behaviour in dynamic and complex environments 
  • Theorem proving, proof assistants and symbolic reasoning 
  • Computer vision  
  • Natural language understanding and translation 
  • Deductive, inductive and abductive reasoning 
  • Learning from experience and existing data 
  • Understanding and emulating emotion 
  • Fuzzy and probabilistic (Bayesean) reasoning 
  • Communication, teamwork, negotiation and argumentation between self-interested agents 
  • Early advances in signal processing (text to speech) 
  • Music understanding and creation 

Like intelligence itself it defies definition.

As a field, it predates Machine Learning and Machine Learning was seen as an early sub-field. Many things that are obvious or no longer considered AI have their roots in the field. Many database models (hierarchical, network and relational) have their roots in AI research. Optimisation and scheduling were early problems tackled under the umbrella of AI. Minsky’s Frame model reads like an early description of Object Oriented programming. LISP, Prolog and many other programming languages and programming language properties emerged as tools for or as a result of AI research.

Neural networks (a sub-field of machine learning) emerged in the 80s in the form of perceptrons and were heavily studied until it was demonstrated that a perceptron was unable to calculate XOR. However, with the invent of error back propagation over networks of perceptrons (a way to systematically train the weights between neurons) it was shown that neural networks have equivalent computational power to universal turing machines (if it can be computed on a turing machine a correctly configured neural network can also implement that same function).

With the invent of Deep Learning in the 2010s the popularity of machine learning has soared as great successes have been achieved using the approach. Due to limits on computational power, traditional neural networks were trained on meticulously human engineered features of the datasets, not the raw datasets themselves. With the progress in cloud, gpus and distributed learning it became possible to create much larger and deeper neural networks. This progressed to the point that large raw datasets could be used directly to train with and get predictions from. In so doing the neural networks extract their own features from the data as part of this process. Many of the recent advances have been achieved due to this (in addition to better neuron activation functions, faster training algorithms, new network architectures).  The successes have also inspired people to use Deep Learning as a means of solving some of the other problems in general AI (as discussed above) and this may explain why a convergence or confusion between AI and Machine Learning is perceived by many.

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