NLP has gone from rule based systems to generative systems with almost human level accuracy along multiple rubrics within 40 years. This is incredible considering we were so far off naturally talking to a computer system even just ten years ago; now I can tell Google Home to turn off my sitting room lights.
In the Stanford Lecture by Chris Manning introduces a Computer Science class to what NLP is, its complexity and specific toolings such as word2vec which enable learning systems to learn from natural language. Professor Manning is the Director of the Stanford Artificial Intelligence Laboratory and is a leader in applying Deep Learning (DL) to NLP.
The goal of NLP is to allow computers to ‘understand’ natural language in order to perform tasks and support the human user to make decisions. For a logic system, understanding and representing the meaning of language is a “difficult goal”. The goal is so compelling all major technology firms have put huge investment into the field. The lecture focuses on these areas of the NLP challenge.
Some applications which you might encounter NLP systems are spell checking, search, recommendations, speech recognition, dialog agents, sentiment analysis and translation services. One key point Chris Manning explains is that human language (either text, speech or movement) is unique in that it is done to communicate something, some ‘meaning’ is embedded in the action. This is not often the case with anything else that generates data. Its data with intent, extracting and understanding the intent is part of the NLP challenge. Chris Manning also lists “Why NLP is hard” which I think we take for granted.
Language interpretation depends on ‘common sense’ and contextual knowledge, language is ambiguous (computers like direct, formal statements!), language contains a complex mix of situational, visual and linguistic knowledge from various timelines. Learning systems we have now do not have a lifetime of learned weights and bias so can only currently be applied in narrow-AI use cases.
The Stanford lecture also dives into DL and how it is different to a human exploring and designing features or signals to then apply to the learning systems. The lecture discusses the first spark of DL with speech recognition from work done by George Dahl and how the DL approach got a 33% increase in performance compared to traditional feature modelling. Professor Manning also talks about how NLP and DL have added capabilities in three segments, namely what he calls Levels; speech, words, syntax and semantics. Tools; parts-of-speech, entities and parsing and Applications; machine translation, sentiment analysis, dialogue agent and question answering. Stating NLP + DL have created a ‘Few key tools’ which have wide applications.
Towards the end of the lecture we explore the ideas around how words are represented as numbers in vector spaces and how this applies to NLP and DL. Word meaning vectors then are usable to represent meaning in words, sentences and beyond.
How AI is Changing The Game Of Business
Artificial intelligence (AI) is changing the game of business at an astonishing rate.
The rapid digital technology advancement has allowed developers to start creating computer systems that have the ability to conduct tasks that normally require human intelligence.
These tasks include (but are not limited to) speech recognition, visual perception, decision-making, and translation. AI has affected different aspects of our lives and the way we are doing business is definitely one of them. In order to understand how AI is changing the game of business, it’s a good idea to highlight a few examples.
Decision-making is one of the most important processes which are part of every business operation. As we said before, AI has a great impact on this process.
Just one error in this process and a company can witness heavy losses and we have all witnessed situations like this. Of course, we all know that in order to make a good decision for your business, you have to analyze data. However, in these modern times, this data is huge and it takes many hours to finish a process like this.
On the other hand, time is crucial for many decisions. Thanks to AI, you can perform big data analysis fast because this type of analysis helps users extract, analyze, and pack unprocessed information in a way that makes decision-making easy.
Marketing is another area where AI can be beneficial for companies. With specific AI tools, business owners and professional marketers will get a chance to understand their consumers in a better way. For example, you can use AI to analyze social media. You can gather this data and change messaging for better effects.
There are many managers that use artificial intelligence to come up with detailed consumer profiles. Also, they can improve digital advertising strategies, and get in touch with their consumers in real-time. To put it in simple words, AI can be used for getting a better insight into marketing as well as in business in general.
The process of hiring employees can be expensive, frustrating and difficult when it’s conducted in a conventional way. Typically, companies are using their Human Resource experts for this job. If you use AI for this purpose, the recruitment process will become easier and you can expect to get the best results.
For instance, there are AI-powered software solutions that can help you categorize and organize job applicants in any way you want — their education, skills, etc. In this way, you can easily make a list of the most suitable candidates without checking their CVs in detail. It’s possible to use AI to schedule interviews in the best possible way too.
It’s impossible for a modern company to thrive and make progress if they don’t have good customer service. Once again, AI is here to help you improve customer service and make everything simpler.
As a matter of fact, artificial intelligence can do this thing while preserving your budget. We are talking about a unique opportunity to improve customer experience without breaking the bank.
Chatbots are a good example of how AI can help. These AI tools can gain data from sales and customer reviews. They can use these things to help customers in choosing and buying products and services.
We should also mention how AI helps companies boost productivity. One of the best ways to get help in this area is to use AI to eliminate repetitive and time-consuming business operations that are conducted on a regular basis. For example, many companies have started AI tools in the form of apps that can help users schedule, cancel and/or reschedule business meetings.
They can also use these tools to record, transcribe, translate and/or share notes during business meetings. Instead of spending time on these tasks, your team can focus on more important activities which will eventually increase productivity.
Even though modern technology is making things easier, it can also cause problems for some businesses. It can be a case when it’s abused by hackers and fraudsters. Unfortunately, there are many people who are trying to take advantage of modern technology in order to commit a crime.
But, it would be much more difficult for them to commit fraud if there’s artificial intelligence involved in your business operations.
AI is able to detect fraud with the help of data analysis focused on different fraudulent activities and behaviors. If the system detects something like this it will immediately alert the supervisor and the potential fraud will be prevented.
These are some of the ways in which AI is changing the game of business. Successful companies are already implementing digital transformation in their operations and the use of AI is part of that transformation. Business development, as well as business growth, is easier when you have Artificial Intelligence on your side.
Artificial Intelligence and Online Privacy: Blessing and a Curse
Artificial Intelligence (AI) is a beautiful piece of technology made to seamlessly augment our everyday experience. It is widely utilized in everything starting from marketing to even traffic light moderation in cities like Pittsburg. However, swords have two edges and the AI is no different. There are a fair number of upsides as well as downsides that follow such technological advancements.
One way or another, the technology is moving too quickly while the education about the risks and safeguards that are in place are falling behind for the vast majority of the population. The whole situation is as much of a blessing for humankind as it is a curse.
In this article, we will be mainly discussing how the AI is being utilized, how the ease of processing data-enabled companies and government agencies to have power over online privacy, and how to stay careful of possible abuses of said power.
Have you ever noticed that every application lately has been asking your permission for personalized ads? These are advertisements from companies that are targeting you. However, how do they know what you may want? This is due to the hard work done by a good number of AIs. They are constantly sifting through and analyzing what you are looking at. Everything is being calculated like: for how long or what you are even messaging sometimes.
Do not be afraid though, they are not reading your secret messages. Reputable companies have these robots looking for some keywords in your conversation. For example, if you are speaking to your friend about bicycles the AI gets excited and offers you an ad from Specialized, Cube, or Ghost offering their brand new mountain bikes. This is how companies like Facebook, Google, Twitter, and others make money. If the product is free – you are the product. In these cases, a product for marketing companies that pay hefty amounts to have their ads on one or all of these platforms.
AI in Everyday Life | The Good
The AI enhances user experience to totally new levels. Unfortunately, analyzing and sifting through huge masses of data is not something the human brain specializes in. To properly process a big chunk of data the companies have to hire a team of individuals. The whole ordeal can take hours, days, weeks, or even months depending on what they are trying to analyze and we are not even taking into consideration the financial expenses.
Even with the professionals, there are moments of subjective bias involved in the result. Humans also tend to flat out ignore or forget a good number of details. An AI though is a different case. Data processing is their specialization. It is something they love doing the most and are happy to continue doing until the end of time. The process is becoming more and more efficient the longer this process goes on. The whole point of artificial intelligence is the ability to enhance their capabilities by finding better and efficient ways to do the same task.
Banking, for example, is the one that has benefitted the most from an AI. As an example, most of the top banks now offer online financial advising. This is done by AIs on the starting level. These programs analyze your spending, give visualized charts, and help save money where possible.
Different exchanges like stock market, foreign exchange market (Forex, FX), cryptocurrency markets, etc. are using the AIs to predict where the market is going to move. Apart from this, the historic data is also always being summarized for personal and corporate use. As it was once said the future is nothing but a repetition of the past.
AI in Military | The Good
The military is very fond of the potential of AI. In 2018 the US announced that they will be implementing artificial intelligence in every branch where it is applicable. The AI is going to help the military in analyzing intelligence, making lightning-fast decisions, automatization of vehicles, weaponry, and logistics as a whole. It is worth noting that the US military is one of the prime places to develop such cutting edge development since they are showering in finances. In 2020 the US congress has approved as much as $718 billion for the Department of Defense out of which $927 million is going to be moving towards the AI and machine learning development.
It is worth noting that the technology has evolved quite a lot during the last decade. Although the good nature of this is debatable the idea that no military personnel has to risk their lives to gather information that can be done using a robot is a huge achievement.
AI in Cyber Security | The Good
Although technically doing cyber crimes is harder now since there are a bunch of protocols available to protect end-user from possible threats it’s also easier because there are a fair number of scripts available that automate very tiresome and complex processes.
Cybersecurity is related to several problems that experts have been trying to deal with as they go along. Once the corporate network becomes large the surface at which the attacks may proceed increases exponentially. This can be done either via breaching application security, network security, or basic social engineering. Today’s systems are protected using multilayer security systems. During the development, the software undergoes an intense testing phase to root out all of the possible hoops that hackers can use to exploit a vulnerability. After the product is finished, there are teams working day and night on enhancing, adding functionality, and patching previous problems. Once the code becomes big enough though the issues are extremely hard to cover and the IT teams have to prioritize some over the others. This is why some of the bugs in your favorite video games are not being patched out quickly and may stay forever if they aren’t game-breaking.
Companies usually build their infrastructure with their intranet protocols that are hiding all of the necessary information from unwelcome guests. However, this means that the network security aspect has to also be covered fully. This is done via firewalls, anti-virus and anti-malware software, data loss prevention, virtual private networks, etc. A good number of programs are written to alleviate this whole process. For example, behavioral analysis is almost solely done with artificial intelligence. This means that there is a software setting and gathering data about how certain individuals are acting while they are inside of the system. The moment there is a suspicious activity the AI either has the right to restrict access temporarily or straight up direct an IT specialist to take a closer look.
Artificial Intelligence is a double-edged sword though. It is important to understand that there are a fair number of precautions that need to be taken before utilizing a service of your choice. It is important to know which company is pulling the strings. Your personal information can and will be utilized to gain more and more funds. However, some companies are going to tell you upfront and some won’t. National security agencies have been targeting these data-gathering companies for years already. Google for example has much more capacities to spy on people than a lot of developed, developing, and underdeveloped countries. The data that goes through their servers is a huge point of interest in a number of both good and bad people. Some of the companies are going to monetize on these offers selling out their collected data to the highest bidder.
AI for Data Collection | The Bad
Most of the time cookies are used to enhance already existing websites. They are the sole purpose why it takes considerably less time to load pages once you’ve already visited them. However, some of these cookies (tracker cookies) are used to track your activity on the internet. This is not even limited to the website that you are using. For example, Facebook cookies get activated every time there is a company API being used. This means that every page that uses Facebook comments, likes, or other services automatically activate their cookies on your computer. This is why you can be commenting something on one website with Twitter addon and then get an ad centered around that very same topic on Twitter. Although I have not mentioned the AI here it is obvious that all of these processes are going through machine learning algorithms that process and dish out the results.
You cannot fully hide your activities on the internet though. People have utilized virtual machines to do different tasks, turned off all of the data collection options, used VPN, but the core idea is that the trail, even though very convoluted at this point, is still going to be leading towards you and it all depends on how badly someone or something wants to find you. Either way, you need to trust someone when you are using the internet.
Why Trust One Company Over the Other?
This leads us to one of the final topics of this article. Why should you trust specific developer companies and not others?
Apart from the general reputation that company gets it’s important to understand that where the firm is based and takes orders from is a big deciding factor. For example, why are Chinese companies not trusted overal? There is a fair argument saying that if you don’t use TikTok due to concerns about Chinese agencies collecting your data you shouldn’t be using Instagram because the US national security departments will also be doing the same. However, things are not that straightforward.
Apple vs FBI | A Case of Integrity
There have been cases where these agencies have demanded access to user data or even straight up requested to have a backdoor into every device. Apple’s dispute against the FBI in 2016 is a prime example of one such instance. To summarize in 2015 the FBI extracted iPhone 5C from a shooter who participated in a December 2015 San Bernardino terrorist attack. They demanded Apple to unlock this device and Tim Cook, the CEO of Apple, has kindly rejected this notion. The company has outlined the importance of preventing terrorism but they couldn’t just give out a backdoor to every iPhone to the FBI. This is in light of the situation that the US government, or the National Security Agency (NSA) in particular, was already outed to be spying on the citizens by a famous whistleblower and founder of WikiLeaks, Edward Snowden.
Apple took this case to court where they used an argument that their customer privacy would be in vain if such instance comes to happen. The FBI didn’t just want one iPhone to be unlocked but they wanted a software backdoor into every past, current, and future smartphone from Apple. The court has ruled in favor of Apple and the case was closed.
This is a happy ending to this scenario. However, we have to keep in mind that the US constitution gives companies an ability to argue such cases in court. Whereas if such thing happens in China, Russia, or any less-democratic country the companies simply do not have the power to go to court and defend their case.
Company Origin Matters
The political nature of the nation is also extremely important. All of the middle to large-sized corporations in China are under the direct supervision of the Chinese Communist Party and share the information with their national security agencies.
Tencent, for example, which is a Chinese company that has developed most of the popular applications like TikTok, WeChat, and has numerous companies under its umbrella-like Riot Games (League of Legends, Valorant, Teamfight Tactics, Legends of Runterra) and Epic Games does not and cannot uphold such standards. More importantly, the CEO and founder father if the company Ma Huateng is a prominent member of the CCP as well as one of the most influential persons across the world. The argument can be made that TikTok doesn’t keep US customer data on Chinese servers but this is a soft barrier. A company can transfer this data for as much as they like and there are a good number of triggers that the Chinese government can pull to make them do their bidding.
A department of this very same company called Tencent Keen Security Lab has researched the security features of Tesla car’s autopilot function. They messed around with sensors and found ways to make the AI go haywire. Tencent is also a 5% stakeholder of Tesla company. This is important information to keep in mind.
The 21st century is a period of technological wonders. Things that were only available in SciFi movies and books are slowly coming to life. While technology is integrating itself into our daily lives it is important to start learning about security aspects as well as the technology that is being utilized. Being picky is not bad when it comes to the usage of the internet. It is strongly encouraged by security professionals across the world. While the digital world may seem like it’s separate from our real lives. The reality stands that it is an integral part and as real as the physical one.
The usage of an AI is making it possible to enlarge operations to scales unseen before. There are some positive sides like ad campaigns but also negative ones like surveillance systems working against consumers. This is all dependent on who’s driving the car and we, the users, can decide which cars to ride and who will be the driver.
This article is designated to highlight some of the uses of AI and how it augments our lives. The core idea is that if users become knowledgable about the subject we can pick and choose which companies to trust. In a capitalist market, consumer trust drives income and the companies will act in any way necessary to keep their user bases.
Introducing Our Low Code Machine Learning Platform
Head of Innovation & Ventures
We are very excited to release the free tier of dunnhumby Model Lab this as part of our partnership with Microsoft. dunnhumby Model Lab is an application that provides automated pipelines for deploying machine learning algorithms and has been used to build millions of models on behalf of our clients.
Sign up here: https://signup.model-lab.dunnhumby.com/
We make it easy to connect your data, clean your data, and run your machine learning pipeline within minutes. You can then take that output and copy right into a notebook for further refinement if needed.
Run all your machine learning from a single platform.
You can create new projects, reference datasets, and create
multiple experiments in just a few clicks!
You can also follow the progress of your machine learning
experiments as they update in real-time.
Automated Tuning of Machine Learning Algorithms
Machine learning algorithms have many parameters that need
to be calibrated based on the data being used.
Some algorithms have over 15 parameters that need to be tuned in order to operate properly, all of them having an impact on each other. It’s billions of combinations. If done by hand, this process can take days without any guarantee you will find the one that will give you the best results.
To reduce the time-to-value and allow data scientists to focus on the good models, Model Lab provides a built-in module that automatically tunes your machine learning algorithms.
Leveraging state-of-the-art Bayesian optimization, Model Lab can tune any machine learning algorithm in a fraction of the time usually required.
Parallel Computing & Resource Optimization
We leverage Kubernetes to run all the models in parallel. This results in a significant boost in performance and considerable reduction in runtime and, therefore, time-to-value.
Each model runs as a container on our cluster, allowing multiple models to be trained simultaneously.
Model Lab comes with a resource optimization module that optimizes
the amount of RAM allocated to each container, allowing us to train as many models as possible in parallel.
See our Medium article on this work here!
Build a classification predictive model in minutes
Classification is one of the most common types of predictive models done at dunnhumby and across our clients. It has been used to predict things like retention insurance, customer churn for retailers and even loyalty.
Building a classification model has become mainstream nowadays and clients expect results very quickly. However, many steps must be completed before delivering a predictive model, which gets in the way of delivering results quickly when performed manually.
Classification is Model Lab’s machine learning experiment that automates the end-to-end process of building a simple but strong classification predictive model. Originally designed to deliver preliminary results within minutes to validate the data, project scope and hypothesis, FastLog has also proven many times to be at par with more complex machine learning algorithms in term of performance for production purposes.
Build a high-level view of your data
Clustering is one of the most common type of analysis done at dunnhumby. It has been used to group stores, products, and customers based on loyalty and lifestyles, with unique behavior, which is different from the respective in other groups.
Clustering has become mainstream at dunnhumby hence, clients expect quick results with interpretations. A lot of steps and methods needs to
be tried before getting the best result in clustering analysis.
Clustering is Model Lab’s experiment for clustering. It automates the end-to-end process of building the best clustering model using given data and very few parameters. FastCluster can perform multiple clustering iteration and identify the best results very quickly.
Data cleaner – Get your data ready to get to work
Cleaner is a utility solution from Model Lab that aims at quickly getting your data ready for the work by cleaning and making it ready. It is a requisite for all our experiments.
Most of the time, the raw data is not in a state that can run machine learning algorithms. Things like missing values, characters, duplicated
rows, etc… can take up to 60% of data scientists time and is the least
Classification – Multiclass
This is an extension of the current Classification experiment to support multi-class problems.
This experiment will be able to predict a continuous target.
Driver Analysis / Non-linear
We will be releasing a new version of our Driver Analysis experiment that leverages non-linear algorithms. Those machine learning algorithms have an advantage over traditional methodologies like univariate analysis, in the sense that they explore both non-linear relationships and interactions in the data.
3D Data Exploration
Data visualization techniques have proven to be sometimes very useful to identify pattern in the data, as our brain is very good a finding patterns. This module will leverage PCA and t-SNE techniques, and provide a 3D visualization of the projected data.
Time Series Modelling
Many problems look at the evolution of certain metrics or target over time. This experiment will allow users to run such analysis and make forecasting over time.
This product is the brainchild of Dr. Victor Robin and is part of dunnhumby Labs, dunnhumby’s new product accelerator.
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