Advent at Grakn Labs: Machine Learning

#GraknLovesTech: 22nd December 2016

Jo Stichbury
Dec 22, 2016 · 3 min read
Source: Adobe Stock photo by martincp

Here at Grakn Labs we love technology. So much so, that, this month, we’ve decided to share our favourite technology moments from 2016. Each weekday during December, we will open a window on our virtual advent calendar, and peek inside to recall some of the greatest innovation or news that the past year has brought us.

Please recommend and share with hashtag #GraknLovesTech if you enjoy our posts. And if you have any favourite links you’d like us share, just leave us a comment or tweet us @graknlabs!

We covered Deep Learning in the #GraknLovesTech blog earlier this month, but today we are looking at Machine Learning. Impacting everything from game playing and online advertising to brain/machine interfaces and medical diagnosis, machine learning explores the construction of algorithms that can learn from and make predictions on data. Rather than following strict program guidelines, machine learning systems build a model based on examples and then make predictions and decisions based on data.

Deep Learning vs Machine Learning

So first things first, we often hear people ask what the difference is between Deep Learning and Machine Learning. So we have gathered a few resources that might help answer that question.

NVidia put out a nice blog post for those wanting a clear explanation of the difference between machine learning and deep learning earlier this year:

Speaking of cats and deep learning, Peter Norvig also has a video explaining how they did it at Google. (Editorial Note: Also because our founder is a huge fan, especially since meeting him in the flesh at the AI conference in NYC earlier this year).

Peter Norvig & Google explains: cat or face?

Our friends at KDNuggets also published a guide:

Andrew Ng’s “Machine Learning” MOOC on Coursera is one of the most popular of their entire corpus. Professor Ng published an interesting article earlier this year about what Artificial Intelligence can and cannot do right now in 2016.

Increased accessibility of ML to the wider community

What we noticed about Machine Learning this year, is that it is not so much about massive breakthroughs but rather it was about making it more accessible to more developers and non-specialists.

In November 2016, Amazon announced the launch of its new Amazon AI platform, to deliver the machine learning that Amazon has developed in-house to a broader developer community. For now, the service only makes three different tools available, but the plan is to add more over time.

Earlier in the year, a partnership on AI was signed by Amazon, DeepMind, Google, Facebook, IBM and Microsoft, to advance public understanding of AI, support best practices and develop an open platform for discussion and engagement.

We are looking forward to 2017, to see what it generates.

Source: Adobe Stock photo by wip-studio

GRAKN.AI is an open-source knowledge graph data platform that brings knowledge ontologies and transactional data together to enable highly intelligent querying of data. Querying is performed through Graql, a declarative, knowledge-oriented graph query language for retrieving explicitly stored and implicitly derived information, and for performing graph analytics and automated reasoning. Grakn and Graql will help you effectively manage and harness large-scale graph data by allowing you to model it expressively, migrate it efficiently, and to draw insightful knowledge from your deep information network.

Find out more about Grakn Labs from our website!


Creators of TypeDB and TypeQL

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