What's the difference between pixels and voxels? The pix and the vox. Duh.
And no, not the movie animation.
I got a bad cold this week and found myself endlessly consulting WebMD for tips and tricks on how to manage it. WebMD has a bad reputations though, for diagnosing every headache as a brain tumor and every cough as lung cancer. The internets are large though and I had to shake off my bias against believing things I find online and remind myself that there's surely some gems out there that have valid medical information. The following are the treasures I dug up while on bed rest.
Here is where Machine Learning comes to the rescue. Machine Learning is the field that studies how to make computers learn. In other words, a Machine Learning algorithm is a computer program that teaches computers how to program themselves so that we don’t have to explicitly describe how to perform the task we want to achieve. The information that a Machine Learning algorithm needs in order to write its own program to solve a particular task is a set of known examples.
For example, for the task of teaching a computer to identify animals, we will show to the computer a bunch of labeled pictures (e.g. this picture is a tiger, this pictures is a cat, etc.), the same way we do it when we teach children. The Machine Learning algorithm will use these samples to identify which are the features that differentiate one animal from another, and with this information it will write its own program to perform the task of identifying animals. Now you can see how enabling computers to learn and enabling computers to write their own code are the same thing.
Therefore, Machine Learning is the way to make computers learn how to perform complex tasks whose processes cannot be easily described by humans, or even tasks that we don’t know how to accomplish (e.g. “I want to calculate how many customers would buy this product” or “I want to make this photo look like a Picasso painting”
I am here to propose a philosophical definition of AI: It is humanity’s quest to understand itself.
Artificial intelligence can be said to be the field of study which tries to give intelligence (maybe consciousness) to machines. It deals with that task of giving machines the abilities of reasoning, planning, learning, natural language processing, perception etc. And also this field can be considered as a combination of fields, computer science, psychology, linguistics, philosophy and neuroscience.
From worms and viruses to DDoS and APTs, in the past quarter of a century the sophistication, impact and scale of cyber-attacks have evolved significantly. However, as cybercrime has become more sophisticated, so has the security against it. These milestone incidents from the past 25 years typify the way in which the threat landscape has evolved and how security has developed in response.
According to Wikipedia...
Computer security, also known as cybersecurity or IT security, is the protection of information systems from theft or damage to the hardware, the software, and to the information on them, as well as from disruption or misdirection of the services they provide.
Chances are, the closest you've come to being hacked is a compromised Facebook or Twitter page with porn strewn all over it and salacious messages being sent out to your extremely religious aunt. You might even have seen claims by shady politicians that their social media accounts were hacked when they accidentally post selfies of themselves entwined in a mistresses' arms on Twitter. I've said before that hacking is a very respectable job in the tech world that just isn't as interesting as Hollywood would like you it think it is.
Sometimes though, breaches of crucial technology is even more outrageous than the directors of Swordfish and The Italian Job could imagine. Plots and subplots straight out of the mind of the most seasoned conspiracy theorists only they're real life events. Here are some of my favorite as we begin this long arduous journey into the inner workings of the cyber security underworld.
Many business owners still feel like getting an app is expensive and difficult. They're worried about the need t build different apps for the various platforms their customers are on- Android, iOS, Windows or Blackberry. They're unsure about building dynamic or static apps and the difficulty of coming up with an upto date, yet timeless design stalls them making that final leap. Moreover, most small business owners don't have the expertise to build apps themselves and the search for competent developers seems daunting.
With all these valid constraints, is it really worth it to pursue this line of technology for you company?
When starting any project you know to enlist the help of an expert to avoid failure. You bring someone into your team that knows better than you what's needed; it smooths out the product creation process, and in the end improves the project outcome. So how come at the end of it all you might still be left staring at a failure? Before you scapegoat your software developer, you might need to have an introspective look at your team's internal macrosystem. Other integral parts of your team failing would prove detrimental to the product's success.