Category: Deepmind

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Unleashing the Revolutionary Power of AI in Data Entry and Processing: Anticipating Unprecedented Advances Before 2025

Data entry and processing is one of the key areas where Artificial Intelligence (AI) is expected to have a major impact in the coming years. With the increasing amount of data being generated every day, the demand for faster and more efficient data processing has never been higher. Fortunately, AI technology is here to help meet this demand and take data entry and processing to the next level.

One of the main advantages of AI in data processing is its ability to automate manual data entry. This means that instead of relying on human data entry clerks, AI algorithms can process and categorize vast amounts of data much more efficiently and accurately. AI algorithms can also identify patterns and relationships within the data, allowing for more comprehensive data analysis.

Another key area where AI is expected to enhance data entry and processing is in natural language processing (NLP). NLP is a subfield of AI that focuses on the interactions between computers and humans in natural language. With advancements in NLP, AI will soon be able to understand and interpret written and spoken human language, making data entry and processing even more seamless.

Before 2025, we can expect to see significant advancements in AI’s ability to process and analyze unstructured data, such as images, videos, and audio. AI algorithms will be able to automatically identify and categorize information within these types of data, making data entry and processing much easier and more efficient. Additionally, AI will be able to process multiple languages, further expanding its reach and impact on data entry and processing.

Another exciting development in the field of AI and data entry and processing is the use of machine learning. Machine learning is a type of AI that allows algorithms to learn and improve over time through experience. With machine learning, AI algorithms can become more accurate and efficient at processing and analyzing data, reducing the risk of human error and improving the overall accuracy of the data.

In conclusion, the next few years will bring significant advancements in the field of AI and data entry and processing. From automating manual data entry to processing unstructured data and utilizing machine learning, AI has the potential to greatly enhance the accuracy and efficiency of data processing. By embracing these changes, we can look forward to a future where data entry and processing is seamless and accurate, providing valuable insights and helping organizations make better data-driven decisions.

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AI Everything

These days it seems like businesses are trying to use AI to do everything. At least for startups, that isn’t far off. Anywhere there is a dataset remotely large enough and an answer that is vaguely definable, companies are putting together a business model to use machine learning to solve the problem. With some incredible successes in areas like image classification and defeating humans at video games, its hard not to be impressed.

One of the best channels for following recent breakthroughs in AI is the 2 Minute Papers YouTube Channel, started by Károly Zsolnai-Fehér, a professor at the Vienna University of Technology in Austria. Károly’s videos combine interesting clips of the programs in action with well-delivered summaries of recent papers illustrating advances in artificial intelligence.

In one of his latest videos, he covers an AI that not only can copy the most successful actions that humans take in video games but can actually improve on those actions to be better than the best human players. So does that mean that AI will be displacing office workers once it learns how to do their jobs better than them? Probably, yes. But maybe not quite how you think it might.

As much of a ‘black-box‘ as AI has been in the past, modern systems are becoming better and better at explaining how they arrived at an answer. This gives human operators predictive capabilities that we didn’t have with systems of the past that could spit out an answer but gave us no indication of how that answer was formulated.

This Forbes article on Human-Centric AI provides some examples of how modern AI systems can be implemented to train employees to do their jobs better and even enjoy their jobs more while doing it! If that doesn’t sound incredible to you, you may be a machine who is only reading this page to improve your search algorithm.

So what does this all mean? A lot of research is showing that AI is actually creating many more jobs than it destroys. So, as long as you’re willing to try and understand the systems that will one day be our overlords, you should be able to upgrade your career and stay employed.

Whether you still want the job that remains is another question entirely.

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Google is Lightyears Ahead in AI

Google’s Deepmind is incredible. In 2016, AlphaGo (one of the Google Deepmind projects) bested the world champion Go player, Lee Sedol in the 2500-year-old Chinese game that many AI experts thought would not be cracked for at least another decade.

AlphaGo won that match against Sedol 4-1, smashing the belief of experts and Go fanatics around the world who knew that a computer couldn’t yet beat a human. There’s a great Netflix documentary on the feat that chronicles the AlphaGo team’s quest to defeat the grandmasters of the ancient game. It reminded me of when I was younger and I watched IBM’s Deep Blue defeat the Grandmaster, Garry Kasparov at Chess, except this was much scarier.

Deep Blue was able to defeat the best Chess players in the world because chess is a game with a relatively small number of possible moves each turn and a computer can essentially ‘brute force’ calculate all the potential moves that a player could make, and easily counter them. Go is a very different game. With an average of 200 potential moves every turn, the number of possible configurations of a Go board quickly exceeds the number of atoms in the entire universe. There are approximately 10^{78} atoms in the universe. A game of Go can last up to 2.08*10^{170} moves, and at each point has at most 361 legal moves. From the lower bound, the number of possible go games is at least {10^{10}}^{48} , which is much, much larger than the number of atoms in the observable universe. This means that brute-forcing the outcome of a Go match is essentially impossible, even with the most powerful supercomputers in the world at your disposal.

In order to surmount this problem, AlphaGo combines three cutting-edge techniques to predict outcomes and play the game better than any human. Alpha Go combines two neural networks – one conducting deep reinforcement learning and the other using a value network to predict the probability of a favourable outcome, with a Monte Carlo search algorithm that works similarly to how Deep Blue worked. The deep reinforcement learning piece works by having the system play itself over and over again improving the neural network and optimizing the system to win the game by any margin necessary. The value network was then trained on 30 million game positions that the system experienced while playing itself to predict the probability of a positive outcome. Finally, the tree search procedure uses an evaluation function to give a ‘tree’ of possible game moves and select the best one based on the likely outcomes.

The architecture of the system is impressive advanced and its ability to beat humans is impressive, however, the most amazing moment in the Netflix documentary comes when the world champion, Sedol, realizes that Alpha Go is making moves that are so incredible, so creative, that he has never seen a human player even think of playing those moves.

Since beating the 9-dan professional two years ago, Google has since iterated its AlphaGo platform with 2 new generations. The latest generation AlphaGo Zero, beat the iteration of AlphaGo that defeated humanity’s best Go player by a margin of 100 – 0. That’s right, the newest version of AlphaGo destroyed the version of AlphaGo that beat the best human player 100 times over. The craziest thing is, the new version of AlphaGo was not given any directions except for the basic rules of the game. It simply played itself over and over again, millions of times, until it knew how to play better than any human or machine ever created.

Courtesy Google’s DeepMind Blog

This awesome video by Two Minute Papers talks about how Google’s Deep mind has iterated over the past few years and how AlphaGo is now exponentially better than the smartest human players and is trained and runs on hardware equivalent to an average laptop.

Courtesy Google’s DeepMind Blog

It is scary and incredible how fast DeepMind is advancing in multiple fields. Right now it is still considered a narrow AI (i.e. it doesn’t perform or learn well in areas outside of its primary domain), however, the same algorithms are being applied to best the greatest humans in every area from medicine to video gaming. In my opinion, it is only a matter of a few years before this system will be able to beat humans at nearly everything that we think we do well right now. We can only hope that Google built in the appropriate safeguards to protect us from its own software.

If you want to learn more about how our future robot overlords think, there’s no better way to get started than by racing an autonomous robocar around a track. Come check out @DIYRobocarsCanada on Instagram and join us on our Meetup group to get involved today!