Very often I am asked when (or whether) we will create a conscious AI.

I scratch my chin and ask “how would you define consciousness?”

The answer usually involves something about “self-awareness.”

I then point out that by that definition, your car is conscious as it has a “check engine light,” which is part of a self-diagnostic loop.

Usually, I point out that consciousness is a subjective phenomenon – it’s “I think, therefore I am” and not “You think, therefore you are.”

I am fascinated with noted physicist Michio Kaku’s explanation on why feedback loops create consciousness.

Dr. Michio Kaku is the co-founder of string field theory, and is one of the most widely recognized scientists in the world today. He has written 4 New York Times Best Sellers, is the science correspondent for CBS This Morning and has hosted numerous science specials for BBC-TV, the Discovery/Science Channel. His radio show broadcasts to 100 radio stations every week. Dr. Kaku holds the Henry Semat Chair and Professorship in theoretical physics at the City College of New York (CUNY), where he has taught for over 25 years. He has also been a visiting professor at the Institute for Advanced Study at Princeton, as well as New York University (NYU).

With Farmbeats, Microsoft is making data-driven agriculture simple and affordable.

Watch this intriguing episode to learn about Farmbeats, (aka.ms/iotshow/farmbeats) a new Azure offering currently in preview and available on Azure Marketplace.

Understand how Farmbeats enables partners to make farmers more efficient by providing visibility into how much water is in the soil, what the soil conditions are, how plants are growing, and more. Learn from Dr. Ranveer Chandra, Chief Scientist, Azure Global, about the amazing solution to the challenge of connecting to farms (leveraging unused TV channels!) developed by Microsoft.

Hear how data is stored in Farmbeats Datahub and analyzed using AI and ML to provide valuable insights like where sensors should be optimally placed. Jeff Hollan, Principal PM Manager, shows how simple it is to use IoT Plug and Play to connect a new sensors, drones, or robots to Farmbeats.

Jeff demonstrates how easy it is for a partner to enable a farmer to view pressure, temperature, and humidity from a sensor on his/her farm using Azure IoT Central.

Learn more aka.ms/iotshow/farmbeats

Try Farmbeats aka.ms/iotshow/farmbeatsonmarketplace

Caleb Curry has everything you need to know to get started as a C++ Programming Software developer / Software engineer — starting off with the super basics and then to intermediate topics.

Time indexes are below the video so you can jump to the section you want.

Timestamps:
1:09 – Intro
9:31 – Installing g++
15:37 – C++ Concepts
22:31 – More C++ Concepts
30:48 – Using Directive and Declaration
37:33 – Variable Declaration and Initialization
40:40 – Using Variables with cout
44:46 – User Input with cin
47:57 – Conventions and Style Guides
56:23 – Intro to Functions
1:01:28 – Intro to Creating Custom Functions
1:09:05 – Pow Function
1:13:13 – Creating Custom Functions
1:22:20 – Creating Void Functions
1:29:11 – Intro to C++ Data Types
1:37:17 – Integral Data Types and Signed vs Unsigned
1:44:38 – Integral Data Types, sizeof, limit
1:49:35 – char Data Type
2:02:21 – bool Data Type
2:06:54 – Floating Point Numbers
2:15:00 – Constant const, macro, and enum
2:21:00 – Numeric Functions
2:28:37 – String Class and C Strings
2:37:47 – get line for Strings
2:40:52 – String Modifier Methods
2:47:56 – String Operation Methods
2:54:36 – Literals
2:59:42 – Hex and Octal
3:04:06 – Operator Precedence and Associativity
3:11:53 – Reviewing Key Concepts
3:17:54 – Control Flow
3:27:32 – If Statement Practice
3:32:33 – Logical and Comparison Operators
3:42:27 – Switch Statement and Enum
3:51:00 – Intro to Loops
3:58:01 – For Loops (How to Calculate Factorial)
4:05:06 – While Loop and Factorial Calculator
4:12:29 – Do While Loop
4:20:04 – Break and Continue
4:25:14 – Conditional Operator
4:29:04 – Intro to Our App
4:33:00 – Creating a Menu
4:38:11 – Creating a Guessing Game
4:45:27 – Intro to Arrays and Vectors
4:54:15 – Working with Arrays
5:04:21 – Passing Arrays to Functions
5:11:11 – Fill Array from Input
5:20:42 – Using and Array to Keep Track of Guessing
5:25:55 – Intro to Vectors
5:33:00 – Creating a Vector
5:36:39 – Passing Vectors to Functions
5:39:55 – Refactor Guessing Game to Use Vectors
5:43:47 – STL Array
5:47:47 – STL Arrays in Practice
5:51:45 – Refactor Guessing Game to Use Templatized Array
5:55:13 – Array vs Vector vs STL Array
5:01:49 – Range Based for Loop
6:07:03 – Intro to IO Streams
6:15:45 – Writing to Files with ofstream
6:24:43 – Readings from Files with ifstream
6:31:13 – Saving High Scores to File
6:39:46 – Functions and Constructors
6:47:53 – Refactoring IO to Function Call and Testing
6:54:31 – Multidimensional Arrays and Nested Vectors
6:59:29 – Const Modifier
7:04:33 – Pass by Reference and Pass By Value
7:11:41 – Swap Function with Pass by Reference
7:14:27 – Intro to Function Overloading
7:19:35 – Function Overloading Examples
7:26:22 – Default Arguments
7:33:24 – Intro to Multifile Compilation
7:40:56 – Multifile Compilation
7:48:15 – Makefiles
7:54:44 – Creating a Simple Makefile
8:01:30 – Intro to Namespaces
8:05:33 – Creating a Namespace
8:10:24 – Intro to Function Templates
8:15:24 – Creating a Function Template
8:18:40 – Overloading Function Templates
8:23:18 – Intro to Object Oriented Programming
8:30:39 – Intro to Structs
8:35:59 – Creating a Struct
8:41:48 – Classes and Object
8:49:58 – Creating a Class
8:52:45 – Working with Objects
9:00:11 – Intro to Constructors
9:05:18 – Constructors and Destructors
9:09:53 – Encapsulation
9:15:40 – Getters and Setters
9:22:47 – Static Data Members
9:29:59 – Intro to Operator Overloading
9:34:07 – Operator Overloading == and +
9:41:13 – Overloading Insert and Extraction Operators
9:49:07 – Friend Functions and Operator Overloading
9:56:14 – Class Across Files
10:03:31 – Inheritance and Polymorphism
10:08:49 – Base Classes and Subclasses Inheritance
10:16:54 – Polymorphism
10:23:15 – Conclusion

TED-Ed explains the science of how viruses can jump from one species to another and the deadly epidemics that can result from these pathogens.

Here’s a story that happened right here in Maryland.

At a Maryland country fair in 2017, farmers reported feverish hogs with inflamed eyes and running snouts. While farmers worried about the pigs, the department of health was concerned about a group of sick fairgoers. Soon, 40 of these attendees would be diagnosed with swine flu. How can pathogens from one species infect another, and what makes this jump so dangerous? Ben Longdon explains.

I always knew that reinforcement learning would teach us more about ourselves than any other kind of AI approach. This feeling was backed up in a paper published recently in Nature.

DeepMind, Alphabet’s AI subsidiary, has once again used lessons from reinforcement learning to propose a new theory about the reward mechanisms within our brains.

The hypothesis, supported by initial experimental findings, could not only improve our understanding of mental health and motivation. It could also validate the current direction of AI research toward building more human-like general intelligence.

It turns out the brain’s reward system works in much the same way—a discovery made in the 1990s, inspired by reinforcement-learning algorithms. When a human or animal is about to perform an action, its dopamine neurons make a prediction about the expected reward.

Here’s a great post about the potential of governments opening up their data sets for the community to explore.

Not only that, but you get a step by step tutorial on how to do it on your own in Python.

This story can be found on NYC Open Data where you can find datasets for everything from 311 complaints, to taxi information, to subway entrance locations, to motor vehicle collisions, and much more.