Surface Capabilities in Google Assistant Skills Adjust your conversation to audio and screen surfaces

This post was published in Chatbots Magazine: Surface Capabilities in Google Assistant Skills.

This post is a part of series about building the personal assistant app, designed for voice as a primary user interface. More posts in series:

  1. Your first Google Assistant skill
  2. Personalize Google Assistant skill with user data
  3. This post

Continue readingSurface Capabilities in Google Assistant Skills Adjust your conversation to audio and screen surfaces

Personalize Google Assistant skill with user data Actions on Google — permissions handling

This post was published in Chatbots Magazine: Personalize Google Assistant skill with user data.

This post is a part of series about building the personal assistant app, designed for voice as a primary user interface. More posts in series:

  1. Your first Google Assistant skill
  2. This post
  3. Surface capabilities in Google Assistant skills

Continue readingPersonalize Google Assistant skill with user data Actions on Google — permissions handling

Your first Google Assistant skill How to build conversational app for Google Home or Google Assistant

This post was published in Chatbots Magazine: Your first Google Assistant skill.

Smart home speakers, assistant platforms and cross-device solutions, so you can talk to your smartwatch and see the result on your TV or car’s dashboard. Personal assistants and VUIs are slowly appearing around us and it’s pretty likely that they will make our lives much easier.
Because of my great faith that natural language will be the next human-machine interface, I decided to start writing new blog posts series and building an open source code where I would like to show how to create new kind of apps: conversational oriented, device-independent assistant skills which will give us freedom in platform or hardware we use.
And will bring the most natural interface for humans – voice.

This post is a part of series about building personal assistant app, designed for voice as a primary user interface. More posts in series:

  1. This post
  2. Personalize Google Assistant skill with user data
  3. Surface capabilities in Google Assistant skills

Continue readingYour first Google Assistant skill How to build conversational app for Google Home or Google Assistant

Moravec’s paradox

There is a discovery in the field of AI, called Moravec’s paradox which tells that activities like abstract thinking and reasoning or skills classified as “hard” – engineering, maths or art are way easier to handle by machine than sensory or motor based unconscious activities.

It’s much easier to implement specialized computers to mimic adult human experts (professional chess or Go players, artists – painters or musicians) than building a machine with skills of 1-year old children with abilities to learn how to move around, recognize faces and voice or pay attention to interesting things. Easy problems are hard and require enormous computation resources, hard problems are easy and require very little computation.

Researchers look for the explanation in theory of evolution – our unconscious skills were developed and optimized during the natural selection process, over millions of years of evolution. And the “newer” skill is (like abstract thinking which appeared “only” hundreds thousands of years ago), the less time nature had to adjust our brains to handle it.

It’s not easy to interpret Moravec’s paradox. Some tell that it describes the future where machines will take jobs which require specialistic skills, making people serving an army of robotic chiefs and analysts. Others argue that paradox guarantees that AI will always need an assistance of people. Or, perhaps more correctly, people will use AI to improve those skills which aren’t as highly developed by nature.

For sure Moravec’s paradox proves one thing – the fact that we developed computer to beat human in Go or Chess doesn’t mean that General Artificial Intelligence is just around the corner. Yes, we are one step closer. But as long as AGI means for us “full copy of human intelligence”, over time it will be only harder.

Where does AI come from? Summary of “Neuroscience-Inspired Artificial Intelligence”

As a technical people, we usually see AI solutions as a bunch of really smart algorithms operating on statistical models, doing nonlinear computations. In general something extremely abstract, what its roots in programming languages.
But, as “neural network” term may suggest, many of those solutions are inspired by biology, primarily biological brain.

Some time ago, DeepMind researchers published paper: Neuroscience-Inspired Artificial Intelligence, where they highlighted some AI techniques which directly or indirectly come from neuroscience. I will try to sum it up, but if you would like to read full version, it can be found under this link:

https://deepmind.com/documents/113/Neuron.pdf

Roots of AI

One of many definitions describes AI as hypothetical intelligence, created not by nature but artificially, in the engineering process. One of the goals of it is to create human-level, General Artificial Intelligence. Many people argue if such an intelligence is even possible, but there is one thing which proves it: it’s a human brain.

It seems natural that neuroscience is used as a guide or an inspiration for new types of architectures and algorithms. Biological computation very often works better than mathematical and logic-based methods, especially when it comes to cognitive functions.
Moreover, if current, still far-from-ideal AI techniques can be found as a core of brain functioning, it’s pretty likely that in some time in the future engineering effort pays off.
At the end, neuroscience can be also a good validation for existing AI solutions.

In current AI research, there are two key fields which took root in neuroscience — Reinforcement Learning (learning by taking actions in the environment to maximise reward) and Deep Learning (learning from examples such as a training set which correlates data with labels). Continue readingWhere does AI come from? Summary of “Neuroscience-Inspired Artificial Intelligence”

3 great TED talks about Artificial Intelligence

Artificial Intelligence is here. Still in its very limited form, but there are more and more places where we, as a humanity are soundly bitten by “intelligent” machines. From the simplest calculators which are hugely smarter than us in maths, to Google Translate which can translate whole sentences, keeping proper grammar and human-like language better than most people in the world.

Yes, AI will take our jobs, it already does. But should we be afraid of it? I believe, we shouldn’t. Instead, we need to adapt to the new reality as it happened many times in humankind history (agricultural revolution, industrial revolution, digital revolution — just name a few).

Some call it another revolution (4th industrial revolution?), some just an evolution which has been happening since the world began. But no matter how you call it, thanks to machines and different kinds of artificial intelligence we’ll for sure reach a new level as a humanity. There is so big potential in us — we all have passion, purpose, dreams.

Now just imagine what can happen to the world when there will be something that can replace us with tedious, repeatable tasks. Or if we could boost our creativity and passion by a help from machines and algorithms which are never distracted and can work unstoppable.

Of course, the transition to “the new world” will be hard. Adaptation will require revolutionary, global changes in how we live. And to start doing this we need to understand where we are and what is coming.
There are already people in this world who are trying to do this. Here are 3 of them, standing in front of us on TED stage and telling us about future of AI and humanity. I highly encourage to invest 45mins to catch-up what they wanted to share with us:

Historical intro to AI planning languages Not only Machine Learning drives our autonomous cars

This is my 2nd publication in field of Artificial Intelligence, prepared as a part of my project in AI Nanodegree classes. This time the goal was to write research paper about important historical developments in the field of AI planning and search. I hope you will like it 🙂.

Planning or more precisely: automated planning and scheduling is one of the major fields of AI (among the others like: Machine Learning, Natural Language Processing, Computer Vision and more). Planning focuses on realisation of strategies or action sequences executed by:

  • Intelligent agents — the autonomous entities (software of hardware) being able to observe the world through different types of sensors and perform actions based on those observations.
  • Autonomous robots — physical intelligent agents which deliver goods (factory robots), keep our house clean (intelligent vacuum cleaners) or discover outer worlds in space missions.
  • Unmanned vehicles — autonomous cars, drones or robotic spacecrafts.

Continue readingHistorical intro to AI planning languages Not only Machine Learning drives our autonomous cars

Understanding AlphaGo How AI beat us in Go — game of profound complexity

One of required skills as an Artificial Intelligence engineer is ability to understand and explain highly technical research papers in this field. One of my projects as a student in AI Nanodegree classes is an analysis of seminal paper in the field of Game-Playing. The target of my analysis was Nature’s paper about technical side of AlphaGo — Google Deepmind system which for the first time in history beat elite professional Go player, winning by 5 games to 0 with European Go champion — Fan Hui.

The goal of this summary (and my future publications) is to make this knowledge widely understandable, especially for those who are just starting the journey in field of AI or those who doesn’t have any experience in this area at all.

The original paper — Mastering the game of Go with deep neural networks and tree search:

http://www.nature.com/nature/journal/v529/n7587/full/nature16961.htm

Continue readingUnderstanding AlphaGo How AI beat us in Go — game of profound complexity

Learning the Artificial Intelligence Why I’m taking AI Nanodegree program at Udacity

There is a time in our life when we mostly learn. Then over the time we learn less and use our knowledge more. And very often it’s not just one-way process but more like a cycle which ends and starts from beginning.
For me now is the moment when a new cycle is slowly starting.

Continue readingLearning the Artificial Intelligence Why I’m taking AI Nanodegree program at Udacity

Iron Man’s Jarvis — is it still a fiction?

Who doesn’t dream about Iron Man’s suit? Infinite power source — Vibranium Arc Reactor, ability to fly and dive thanks to Repulsors and oxygen supplies, almost indestructible single-crystal titanium armor with extremely danger weaponry.

Since we’re still years or even decades (are we?) from having at least prototype of flying metal suite, there is one piece of it which can be closer than we think.

JARVIS

While Vibranium Arc Reactor is a heart of Iron Man suit, the equally important thing is its brain — Jarvis.
Jarvis is a highly advanced computerized A.I. developed by Tony Stark, (…) to manage almost everything, especially matters related to technology, in Tony’s life.” Does it sound familiar? Continue reading “Iron Man’s Jarvis — is it still a fiction?”