This video represent a list of top 5 challenges that are common to many in the machine learning and data science community.
Scarcity of Data
The more data that can be used for modeling and predictions, the better.
This isn’t a problem for big companies, such as Facebook and Google. However, for many others, lack of sufficient data can limit their results rendering machine learning less productive.
Vague questions will not result in substantive results. Data science is about recognizing patterns. So, clear questions are fundamental to defining what types of patterns to analyze.
Unclear Representation of Data
For a data scientist, the resulting work needs to be represented to the end user in meaningful ways. There is more room for libraries to make life easier to better represent data.
Computing millions of lines of code over and over can be expensive. But it should get less expensive in the future.
Machine learning Algorithm selection
Currently, the biggest challenge in data science is the selection of the right algorithms. It’s important to understand the algorithms as well as possible to determine which ones will most benefit your project.
Machine learning is recognizing patterns in data and can help people support decisions.
Some practical ways that media and entertainment companies can apply artificial intelligence and machine learning is with contract management processing, budgeting, on-boarding production freelancers and standardization of digital asset management.
In brief, machine learning can help make an institution’s accumulated knowledge accessible to stakeholders.
The future of artificial intelligence (AI) encompasses risks and challenges.
The public perception of AI includes a fear that artificial intelligence might take away their job. And it’s true: AI is a tool people can use to operate business more efficiently. Of course some new jobs are created to support AI systems, but more people will lose jobs than new jobs are anticipated to be created.
Other risks for AI include data privacy concerns, government laws and regulations, and company stakeholders who don’t understand the technology.
AI is moving forward in spite of the challenges, but how smooth the transition will be for such an important shift in global technology and human labor is undetermined.
This NVIDIA video describes deep learning as the fastest-growing field in artificial intelligence, helping computers make sense of infinite amounts of data in the form of images, sound, and text.
Using multiple levels of neural networks, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.
Specific real-world deep learning applications include the ability to analyze in one month, what used to take 10 years; voice-to-text technology; robotics; autonomous cars; and winning chess against a world champion.
Every industry will be impacted by deep learning, and many businesses are already delivering new products and services based on this new way of thinking about data and technology.
Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is.
AI is designed so you don’t realize there’s a computer calling the shots. But that also makes understanding what AI is — and what it’s not — a little complicated.
In basic terms, AI is a broad area of computer science that makes machines seem like they have human intelligence.
It includes programming a computer to drive a car by obeying traffic signals.
The term “artificial intelligence” was first coined back in 1956 by Dartmouth professor John McCarthy. He called together a group of computer scientists and mathematicians to see if machines could learn like a young child does, using trial and error to develop formal reasoning. The project proposal says they’ll figure out how to make machines “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
But during the past few years, a couple of factors have led to AI becoming the next “big” thing: First, huge amounts of data are being created every minute. In fact, 90% of the world’s data has been generated in the past two years. And now thanks to advances in processing speeds, computers can actually make sense of all this information more quickly. Because of this, tech giants and venture capitalists have bought into AI and are infusing the market with cash and new applications.
When it comes to AI, a robot is nothing more than the shell concealing what’s actually used to power the technology.
Siraj Raval discusses artificial intelligence (AI) and blockchain.
He notes that machine learning – AI – is not new. It’s been around since the 1950s.
However, in recent years, one facet of AI, neural networks, has engendered the model of deep learning, which has outperformed all prior machine learning models.
Hence, all big AI advancements noways are coming from neural networks.
In contrast to AI, he describes blockchain as an immutable data structure that no one owns.
Raval depicts AI and blockchain as a ying and yang.
In Chinese philosophy, yin and yang describes how opposite or contrary forces are actually complementary, interconnected, and interdependent in the natural world, and how they give rise to each other as they interrelate to one another.
More specifically, he describes AI (“yin”) as probabilistic (subject to or involving chance variation) and changing, using algorithms to guess at reality.
He describes blockchains (“yang”) as determininistic (forces and factors cause things to happen in a way that cannot be changed) and permanent, using algorithms and cryptography to record reality.
Raval refers to our personal data as the most valuable asset we have and yet we give it away for free to companies in exchange for free services.
Hence, decentralized apps are an important idea, which can also be called web 3.0, which he considers one of the most exciting developments today.
More specifically, he describes decentralized autonomous organizations (DAO) or decentralized apps (Dapp) as a solution to data management.
DAO is a computational process that runs autonomously, on a decentralized infrastructure with resource manipulation. “It’s code that can own stuff.”
Instead of one central authority owning it, the participants have ownership.
Raval states that the future is AI and blockchain.
He also cautions against the downside of decentralized apps using AI and blockchain, such as machines running independent from the control of humans.
Raval ends with a rap about AI and blockchain.
Siraj Raval is a Dapp developer & entrepreneur. He is founder of a crowdfunding platform for developers called Havi, has developed several iOS apps including Meetup, and has worked on a host of open source work.