We all know how much impact and potential AI has, but many don’t know why it’s so much powerful. In this article I am going to discuss about why AI is a game changer. Let’s start the journey.
What is AI?
Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.
Machine learning is one of the most common types of artificial intelligence in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of artificial intelligence are algorithms that appear to “learn” over time, getting better at what they do the more often they do it. Feed a machine learning algorithm more data and its modeling should improve. Machine learning is useful for putting vast troves of data — increasingly captured by connected devices and the internet of things — into a digestible context for humans.
Deep learning is an even more specific version of machine learning that relies on neural networks to engage in nonlinear reasoning. Deep learning is critical to performing more advanced functions, such as fraud detection. It can do this by analyzing a wide range of factors at once. For example, for self-driving cars to work, several factors must be identified, analyzed and responded to at once. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5–10 seconds. All this information is calculated side by side to help a self-driving car make decisions like when to change lanes.
Deep learning has a great deal of promise in business and is likely to be more commonly used soon. Older machine learning algorithms tend to plateau in their capability once a certain amount of data has been captured, but deep learning models continue to improve their performance as more data is received.
Artificial intelligence and business today
Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, humans can use artificial intelligence to help game out possible consequences of each action and streamline the decision-making process.
Business is all about finding the right customers, understand their needs and deliver the product they want. AI is playing a huge role in that. Let’s see ho some companies like Netflix, Amazon, YouTube etc. to grow their bussiness very efficiently.
AI and RPA
RPA is a technology which if growing at a rapid rate and integrating it with AI is solving many problems. While they have a lot in common, artificial intelligence and Robotic Process Automation are two different technologies. RPA is incredibly efficient, but it does only what the user or programmer tells it to do, while an AI can teach itself. RPA can automate all the rule-based tasks, and AI can bridge the gap where RPA falls short.
RPA deals with structured data. AI is used to gather insights from semi-structured and unstructured data in text, scanned documents, webpages, and PDFs. AI brings value by processing and converting the data to a structured form for RPA to understand.
The two technologies can support each other and can coexist in integration to form a more robust platform for intelligent automation — automating any front- or back-office business process and orchestrating work across combined human-bot teams.
80% of stream time is achieved through Netflix’s recommender system, which is a highly impressive number. Moreover, Netflix believes in creating a user experience that will seek to improve retention rate, which in turn translates to savings on customer acquisition (estimated $1B per year as of 2016).
Netflix uses a variety of rankers mentioned in its paper, though specifics of each model’s architecture is not specified. Here is a summary of what they are:
Personalised Video Ranking (PVR) — This algorithm is a general-purpose one, which usually filters down the catalog by a certain criteria (e.g. Violent TV Programmes, US TV shows, Romance, etc), combined with side features including user features and popularity.
Example of PVR generated items
Top-N Video Ranker — Similar to PVR except that it only looks at the head of the rankings and looks at the entire catalog. It is optimised using metrics that look at the head of the catalog rankings (e.g. MAP@K, NDCG).
Example of Top-N ranker generated titles
Trending Now Ranker — This algorithm captures temporal trends which Netflix deduces to be strong predictors. These short-term trends can range from a few minutes a a few days. These events/trends are typically:
- Events that have a seasonal trend and repeat themselves (e.g. Valentines day leads to an uptick in Romance videos being consumed)
- One-off, short term events (e.g. Coronavirus or other disasters, leading to short-term interest in documentaries about them)
Example of Trending Now ranker generated titles
Continue Watching Ranker — This algorithm looks at items that the member has consumed but has not completed, typically:
- Episodic content (e.g. drama series)
- Non-episodic content that can be consumed in small bites (e.g. movies that are half-completed, series that are episode independent such as Black Mirror)
The algorithm calculates the probability of the member continue watching and includes other context-aware signals (e.g. time elapsed since viewing, point of abandonment, device watched on, etc).
Example of Continue Watching ranker generated titles
Use of RNNs in time-sensitive sequence prediction. Netflix uses a particular member’s past plays alongside the contextual information and use this to predict what the member’s next play might be. In particular, using continuous time together with discrete time context as input performs the best.
Chinese company Alibaba is the world’s largest e-commerce platform that sells more than Amazon and eBay combined. Artificial intelligence (AI) is integral in Alibaba’s daily operations and is used to predict what customers might want to buy. With natural language processing, the company automatically generates product descriptions for the site. Another way Alibaba uses artificial intelligence is in its City Brain project to create smart cities. The project uses AI algorithms to help reduce traffic jams by monitoring every vehicle in the city. Additionally, Alibaba, through its cloud computing division called Alibaba Cloud, is helping farmers monitor crops to improve yield and cuts costs with artificial intelligence.
Alphabet — Google
Alphabet is Google’s parent company. Waymo, the company’s self-driving technology division, began as a project at Google. Today, Waymo wants to bring self-driving technology to the world to not only to move people around, but to reduce the number of crashes. Its autonomous vehicles are currently shuttling riders around California in self-driving taxis. Right now, the company can’t charge a fare and a human driver still sits behind the wheel during the pilot program. Google signaled its commitment to deep learning when it acquired DeepMind. Not only did the system learn how to play 49 different Atari games, the AlphaGo program was the first to beat a professional player at the game of Go. Another AI innovation from Google is Google Duplex. Using natural language processing, an AI voice interface can make phone calls and schedule appointments on your behalf. Learn even more about how Google is incorporating artificial intelligence and machine learning into operations.
Not only is Amazon in the artificial intelligence game with its digital voice assistant, Alexa, but artificial intelligence is also part of many aspects of its business. Another innovative way Amazon uses artificial intelligence is to ship things to you before you even think about buying it. They collect a lot of data about each person’s buying habits and have such confidence in how the data they collect helps them recommend items to its customers and now predict what they need even before they need it by using predictive analytics. In a time when many brick-and-mortar stores are struggling to figure out how to stay relevant, America’s largest e-tailer offers a new convenience store concept called Amazon Go. Unlike other stores, there is no checkout required. The stores have artificial intelligence technology that tracks what items you pick up and then automatically charges you for those items through the Amazon Go app on your phone. Since there is no checkout, you bring your own bags to fill up with items, and there are cameras watching your every move to identify every item you put in your bag to ultimately charge you for it.
There are millions of examples. These few were example of some very companies. RPA with can be a boom in technology.
I hope the article was able to give you a brief insight. If you have any feedback or suggestion, you can comment below.
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Rajnish Mishra - ARTH - ARTH - The School of Technologies | LinkedIn
I have worked on various projects in the field of Machine Learning, Deep Learning, GAN, CNN, Devops Assembly Line…
Thank You very much for your such a valuable time.