AlphaAI 解決方案介紹

A learning robot to open the black box of artificial intelligence

PRESENTATION #1

1. General presentation : Why ?, What ?, and How to teach AI?

2. AI initiation activities : The different levels of autonomy of a machine 3. Going further : Python programming of the robot and neural networks

Website: https://learningrobots.ai

WHY teach AI to everyone?

The future of WORK

The future of HUMANITY

Priority for SCHOOL and PROFESIONNAL TRAINING

WHAT to teach about AI? (elementary, secondary, higher education) Let’s open wide the BLACK BOX of « Artificial Intelligence »

Algorithms  Visualize

Data

Implementation

 Manipulate

 Code

HOW can we talk about AI algorithms in a concrete way?

A GRAPHICAL INTERFACE

▶ see the details of AI algorithms A LEARNING ROBOT

▶ learns in front of our eyes

A PEDAGOGY

▶ workshops, conferences, etc.

The graphical interface

Visualisation

Control

The robot and the arena 3D printed protective shell in 8 colours

On-board computer :Raspberry Pi Zero(linux, Python, etc.)

Modular arena

camera buzzer

2 wheel drive

4 multi-colour LEDs 5 IR line tracking sensors

ultrasound

Soon, multiple robots!

Robotis Partner: DMS Education Sales kick-off: Oct. 2021 AlphAI robot (purchased from Waveshare and customized)

Sales kick-off: Oct. 2020 I3D printer Partner: Didastel Sales kick-off: Oct. 2021 AlphAI software

More robots in Education…

And tomorrow in Domestic & Industry…

Buddy Partner: Blue Frog Robotics

Thymio Partner: EPFL / Mobsya

The "brick" of our activities: the mini learning scenario Supervised Learning

Manipulate

Robot race

To your imagination !

Intruder detection

K nearest neighbors

Line tracking

Blocked vs. Motion

?

Code AI

Code

Obstacle avoidance Football

Image recognition

Manual edition

Visualize

Reinforcement Learning

def take_decision(x): if X_training is None: return 0 return nearest_neighbor_decision( X_training, y_training, x)

Code Reward def reward(state): print('Current state is:') print(state) return state['speed']

An activity for all: autonomous robot race

Activities: practical work, workshops, conferences https://learningrobots.ai/resources/?lang=en#download_activities

Introduction to AI activities Introduction to AI : 1) Remote control 2) Programmin 3) Supervised learning 4) Reinforcement learning Ethics of AI

Robot race (supervised) Learning "by your imagination

K-nearest neighbour algorithm

Coding and expert activities

1) Programming remote control 2) « Classic » programming

3a) Programming K nearest neighbors  organize a robot race !

3b) Discovery of neural networks with the « intruder detection »

4) Programming Q-learning

From elementary school to university!

1ère NSI

Origins: February 2018, ULIS class

1ère / Tale STI2D

Philosophy in EC1

Learning Robots guest exhibitor on the Ile-deFrance Region stand; Presentation to Mrs Valérie Pécresse

Viva Tech 2021

First uses by customers and partners 150 clients, 460 robots

Help to get started: Exchange server (Discord)

Metz-Nancy (eLab Schoenbeck)

submission of a scientific publication by EPFL

Verbatims of teachers and prescribers "I am very pleased with this hardware and software that shows the concepts of artificial intelligence in a very easy way. I hope to be able to continue using it in the following years."

Michael Balandier, Professor in Digital Sciences Charleville-Mézières (France)

"As far as the pedagogical value is concerned, it is without question, these machines are a great support and the students have been hyper responsive with it."

C. Maury, Prof. NSI Pont-à-Mousson (FR)

Viva Tech exhibition, June 2021 in Paris: Encouragement from Valérie Pécresse (President of the Ile de France Region) et

"The AlphAI solution supported by the EduUp commission of the French Ministry of Education makes the effects of programming an AI tangible in line with the development of educational robotics."

Alain Thillay Directorate of Digital Education Ministry of National Education (France)

"I wanted to remind you once again of the quality of your follow-up and the support you give us: AI teaching, if well implemented, will be a real plus for our young people!"

Vincent Honorat Teacher in pre-college classes Aix-en-Provence (France)

Antoine Petit (CNRS director)

PRESENTATION #2

1. General presentation : Why ?, What ?, and How to teach AI?

2. AI initiation activities : The different levels of autonomy of a machine 3. Going further : Python programming of the robot and neural networks

What is "Artificial Intelligence"?

Intelligence (Latin intelligentia, from intelligere, to know; inter- ("between") lĕgĕre ("to gather, choose, read")) Petit Robert 1: "All the mental functions whose object is conceptual and rational knowledge".

Pragmatic definition: "All of man's intellectual faculties".

Artificial (from Latin artificialis "made with art") Petit Robert 1: "Produced by the work of man and not by nature".

Artificial Intelligence "Reproduction by human labour of the intellectual faculties of man!Petit Robert: "A set of theories and techniques used to create machines capable of simulating human intelligence".

Artificial Intelligence is a project!

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

- Dartmouth AI Project Proposal; J. McCarthy et al.; Aug. 31, 1955.

… and by misuse of language, some achievements

4 levels of autonomy for the AlphAI robot Level 1: Remote control

Orders

No autonomy

Level 1: Remote control Qualities of the robot • Motors • Electronic card • Wireless connection to the computer • Program on the computer to receive orders Similarities with living things • Motors ↔ Muscles • Electronic board, communication, etc. ↔ Nerves Qualities that the robot does not have • The robot has no autonomy • The human decides each action of the robot ("orders")

4 levels of autonomy for the AlphAI robot Level 1: Remote control

Level 2: Programmation

Orders

Program

No autonomy

Autonomy

Level 2: Programming Qualities of the robot (in addition to the above) • Sensors • Programmed action decisions based on sensor status allows for autonomous movement Similarities with living things • Sensors ↔ Sense organs • Decision-making program ↔ Brain = command centre Qualities that the robot does not have The robot only does what the human has planned. The human has planned in advance all the scenarios and what to do.The robot always does the same thing, does not adapt, has no initiative, etc.

4 levels of autonomy for the AlphAI robot Level 1: Remote control

Level 2: Programming

Level 3: Learning Supervised

Orders

Program

Examples

No autonomy

Autonomy

Autonomy Memory Learning

Level 3: Supervised learning = “by Imitation” Qualities of the robot (in addition to the above) • Memorises examples given by humans • Learning programme to modify its "brain".

Necessary qualities of training data • No errors • Completeness of situations • Quantity Similarities with living things • Memory / Accumulation and replay during sleep • Learning / Changing connections • Repetition / Perseverance

Qualities that the robot does not have The robot only reproduces the behaviour dictated by the human. It has no initiative.However, there can be unexpected things, especially when the training has not been sufficient.

Examples of supervised learning

Examples of supervised learning

Examples of supervised learning

https://www.google.com/doodles/c elebrating-johann-sebastian-bach

Examples of supervised learning ++ GPT-3 by OpenAI, 2020

Feeling unproductive? Maybe you should stop overthinking.

In order to get something done, maybe we need to think less. Seems counterintuitive, but I believe sometimes our thoughts can get in the way of the creative process. We can work better at times when we "tune out" the external world and focus on what's in front of us.

Definition #1: Creative Thinking (CT) is the act of using your mind to come up with ideas that are not already present in reality or that have never existed before. It is a form of mental activity that involves the use of logic and reason.

Definition #2: Over-Thinking (OT) is the act of trying to come up with ideas that have already been thought through by someone else. OT usually results in ideas that are impractical, impossible, or even stupid.

Now, how do these two concepts relate to each other? Well, if you're like me then you probably find yourself doing both at times. You might be working on a project all day long, but you still feel as though you're just going through the motions. This is because you're thinking too much!

This is where OT comes into play. When you think too much, your brain starts to shut down certain areas of your consciousness which causes you to become more passive and less productive. On top of this, your attention span decreases due to being constantly bombarded with information.

So how do we combat this?

The answer is simple: Creative Thinking.

Creative thinking is the exact opposite of over-thinking. Instead of using your brain to analyze everything, you use it to create something. This can be anything from writing a story, to drawing a picture, to even building a model.

When you engage in creative thinking, your brain starts working more efficiently. It becomes more active and more open to new ideas. It also helps you think outside the box and look at things from a different perspective.

So how does this all tie into productivity?

I've been thinking about this lately, so I thought it would be good to write an article about it.

Well, if you're a creator then you should be engaging in creative thinking on a regular basis. The more you do it, the better your brain becomes at thinking up ideas. This makes it easier for you to work on your projects because you won't get stuck as often.

In addition to this, your brain will be more alert and aware of its surroundings. This means you'll be more productive in general because you won't be tired or distracted as easily.

So how do you get into the habit of creative thinking?

So what exactly does this mean? Well, for starters, let's start with some definitions.

Well, the best way is to just do it!

The more you practice, the better you'll get. It's really that simple.

I've found that the best way to get into the habit of creative thinking is to engage in it daily. This can be done by setting aside a certain amount of time each day (or week if you need more time) to do something creative. You can start off small by committing to do one activity each day. For example, you could spend 15 minutes drawing each day or spend an hour writing. Once you get into the habit of doing one creative activity each day, you can increase the amount of time you spend.

I would recommend not going over two hours each day though. If you do this, then you're probably just wasting time and not actually engaging in creative thinking.

You see, creative thinking is all about having fun. If you spend too much time on it, then it stops being fun and it just feels like work. If you do find yourself in this situation, then I recommend that you go back to spending less time on it and try to engage in other creative activities instead.

For example, if you're writing for too long then go and draw something. Or if you're drawing for too long then go and write something.

The key is to have fun and enjoy the process of creating something.

In addition to this, you should also try to engage in other forms of creative thinking as well. For example, if you're writing stories then try drawing a picture to go along with it. Or if you're drawing a picture then try writing a story about it.

The more you can cross-pollinate your creative thinking, the more ideas you'll be able to come up with. This is because your brain makes connections between different forms of creative thinking.

For example, if you're writing a story about a character in a desert and you draw a picture of that character, your brain will make a connection and give you ideas on what to have that character do next in your story. Or if you're drawing a picture of a character from a story and you write a poem about their feelings, your brain will make a connection and give you ideas on how to draw that character better.

In addition to this, engaging in multiple forms of creative thinking will also help you get through creative blocks. If you're stuck on a story you're writing, then drawing a picture or writing a poem can sometimes give you the extra push you need to get past that block.

Creative thinking isn't just for artists though.

Anyone can get into the habit of engaging in creative thinking. For example, if you're good at math then try writing a poem about a number or drawing a picture of a mathematical equation. If you're good at writing poems then try drawing a picture to go along with it. If you're good at drawing pictures then try writing a story about it.

You can even take this further by combining different forms of creative thinking.

For example, if you're writing a story about a character in a desert and you draw a picture of that character, you could write a poem about the feelings that the character has while they're in the desert.

Or if you're drawing a picture of a character from a story and you write a poem about their feelings, you could draw a picture to go along with that poem.

The possibilities are endless.

The important thing is to keep an open mind and try new things.

Don't just limit yourself to one form of creative thinking.

Expand your mind and explore all the possibilities that are out there. I wish you the best of luck on your journey and I hope that you achieve all of your creative dreams!

"Fin."

Examples of supervised learning ++ https://openai.com/blog/dall-e/

General public activity with supervised app.: robot race

An easy-to-understand algorithm: the K nearest neighbours Python coding

1 - K nearest neighbors

import math train_sensors = train_decisions = None def learn(X_train, y_train): global train_sensors, train_decisions train_sensors, train_decisions = X_train, y_train def take_decision(sensors): if train_sensors is None: return 0 return nearest_neighbor_decision(train_sensors, train_decisions, sensors)

Right pixel intensity

Turn right

def distance(a, b): return math.sqrt((b[0]-a[0])**2 + (b[1]-a[1])**2)

Straight

def all_distances(a, train_sensors): return [distance(a, b) for b in train_sensors] def find_minimum(dlist): value_min = math.inf index_min = None for i in range(len(dlist)): if dlist[i] < value_min: value_min = dlist[i] index_min = i return index_min

Turn left

Left pixel intensity

def nearest_neighbor_decision(train_sensors, train_decisions, a): dlist = all_distances(a, train_sensors) idx_min = find_minimum(dlist) return train_decisions[idx_min]

Introduction to artificial neural networks: Artificial intelligence is inspired by the brain

Neurons in the brain respond to stimuli Olfactory brain

Odeur

Stimulus

Neurons

Behaviour

Artificial neural network ("deep learning") Sensors

« Brain »

Action

Supervised Learning Sensors

« Brain »

Action Learning

Examples

4 levels of autonomy for the AlphAI robot Level 1: Remote control

Orders

No autonomy

Level 2: Programming

Level 3: Learning Supervised

Program

Examples

Autonomy

Autonomy Memory Learning

Level 4: Learning by Reinforcement

Reward system Autonomy Memory Learning+ Explorations

Reinforcement Learning Sensors Reward

« Brain » Learning

Action

Level 4: Reinforcement learning = trial and error Qualities of the robot (in addition to the above) • Learns what rewards they can get • Anticipates rewards several 'moves' in advanceTrials actions ("exploration")  Can outperform humans! (e.g. chess, Go) Similarities with living things • Trial and error / mistakes are useful!

• Exploration ↔ "CuriosityForced" exploration ↔ parents/teachers • Anticipation of future rewards ↔ sense of effort Qualities that the robot does not have • Learning is more autonomous, but still determined by humanwritten algorithms. Still no initiative in the 'human' sense (awareness, understanding of the meaning of things, capacity for abstraction, etc.)

• Moreover, the robot does not know how to set its own goals. The human being sets the reward system.

Examples of reinforcement learning AlphaGo par Google DeepMind vs. Lee Sedol, 2016

Examples of reinforcement learning AlphaStar par Google DeepMind vs. « Mana », 2018

Examples of reinforcement learning Pepper par SoftBank Robotics, 2018

So, Artificial Intelligence?

A great new tool!!!

The human in question!!?

Mastering it so as not to be mastered

Understanding to react

Opening all the black boxes of Artificial Intelligence with the AlphAi robot Let’s open the BLACK BOX of Artificial Intelligence !!

Programming = INSTRUCTIONS

Addressing the societal and philosophical issues raised by A.I.

Machine Learning = automatic learning from DATA

But how does it work?! = understand ALGORITHMS

Ethics of AI Workshop: Biases in AI

TEST TRAIN

Ethics of AI Workshop: Let an AI system take decision?

PRESENTATION #3

1. Présentation Générale : Pourquoi ?, Quoi ?, et Comment enseigner l’IA ?

2. Activités d’initiation à l’IA : Les différents niveaux d’autonomie d’une machine 3. Aller plus loin : Programmation Python du robot et réseaux de neurones

API Python def open_api() Ouvre une connexion au logiciel pour communiquer les commandes. Cette fonction est appelée automatiquement à l’import du module alphai. def bluetooth_list()→list[str] Renvoie la liste des robots AlphAI connus par le Bluetooth de l’ordinateur.

def motor(left,right) Envoie une commande de vitesse aux moteurs gauche et droit. def wait_for_key()→list[str] Attend que l’utilisateur presse ou relâche une touche, et renvoie la liste des touches du clavier pressées simultanément.

def connect_bluetooth(robot_number: int)→bool

def get_blockade()→bool Renvoie True si le robot est bloqué (par un obstacle) ou False sinon.

Tente une connexion au robot spécifié en utilisant le Bluetooth. Le robot doit être dans la liste des robots connus par l’ordinateur. Renvoie True si la connexion est un succès, False sinon.

def get_distance()→float Renvoie la distance en cm devant le robot mesurée avec le capteur ultrason.

def connect_wifi()→bool Tente une connexion au robot auquel l’ordinateur est connecté en Wi-Fi. Renvoie True si la connexion est un succès, False sinon.

def get_infrared()→list[float] Renvoie le signal infra-rouge mesuré par les 5 capteurs infrarouges dirigé vers le sol.

def disconnect() Ferme la connexion avec le robot.

def set_camera(config:str) Définit la taille de l’image caméra. config doit être une des valeurs suivantes 2x1, 4x3, 8x6, 16x12, 21x16, 32x24, 64x48.

def is_connected()→bool Renvoie True si le robot est connecté ou False sinon.

def get_camera()→list[list[list[int]]] Renvoie l’image caméra du robot.

1) TP Téléguidage

et

2) TP Programmation caméra if

:

forward()

elif

turn_left()

else: #

turn_right()

:

3a) Apprentissage Supervisé : K plus proches voisins

Code élève

Right pixel intensity

def learn(X_train, y_train): ...

Turn right

def take_decision(sensors): ... return ...

Straight

Turn left

Left pixel intensity

3a) Apprentissage Supervisé : K plus proches voisins Corrigé (1 plus proche voisin) import math train_sensors = train_decisions = None def learn(X_train, y_train): global train_sensors, train_decisions train_sensors, train_decisions = X_train, y_train def take_decision(sensors): if train_sensors is None: return 0 return nearest_neighbor_decision(train_sensors, train_decisions, sensors) def distance(a, b): return math.sqrt((b[0]-a[0])**2 + (b[1]-a[1])**2) def all_distances(a, train_sensors): return [distance(a, b) for b in train_sensors]

def find_minimum(dlist): value_min = math.inf index_min = None for i in range(len(dlist)): if dlist[i] < value_min: value_min = dlist[i] index_min = i return index_min def nearest_neighbor_decision(train_sensors, train_decisions, a): dlist = all_distances(a, train_sensors) idx_min = find_minimum(dlist) return train_decisions[idx_min]

Généralisation K plus proches voisins def find_k_minima(dlist, k): k = min(k, len(dlist)) index_min = [] for i in range(k): idx = find_minimum(dlist) index_min.append(idx) dlist[idx] = math.inf def k_nearest_neighbors_decision(train_sensors, train_decisions, a, k): dlist = all_distances(a, train_sensors) index_min = find_k_minima(dlist, k) votes = [0] * max(train_decisions) for idx in index_min: decision = train_decisions[idx] votes[decision] = votes[decision] + 1 majority_decision = find_maximum(votes) return majority_decision

Généralisation ND def distance(a, b): n = len(a) s = 0 for i in range(n): s = s + (b[i]-a[i])**2 return math.sqrt(s)

3b) App. Supervisé / Réseaux de Neurones : Détection d’intrus

?

4) App. par Renforcement : programmation du Q-learning

take_decision(observation) learn(observations, actions, rewards)

calcul_recompense(data…)

example actions

PRESENTATION #4

1. General presentation : Why, What, and How to teach AI?

2. Introduction to AI activities : The different levels of autonomy of a machine 3. Going further : Python programming of the robot and neural networks 4. A few more examples!

Saliency maps

Reinforcement learning: Q-learning equation, similarities with human learning 4 - Simple reinforcement

Experiments underway in elementary schools:"Pupils' apprehensions about their own learning Learn by trials and errors

Exploration (“curiosity”) environme nt

agen t

Master guidance Learn through repetition Memory replay (learning during “sleep”)

environme nt

Update:

agen t

Future rewards expectation (“perseverance”)

Football learning and reward and error graphs Football