The Automated Writing Assistance Landscape in 2021

Automated Writing

Automated writing assistance — a category that encompasses a variety of computer-based tools that help with writing — has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today and consider where things might be going.

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How Machine Learning and AI personalize automotive applications

Cityscape on device

New information and solutions bring new experiences to users, and these solutions or information are beneficial to both the user and the provider. The taste of the coming exciting news is the main factor keeping users on the path to following new services. For example, suppose you are looking to travel and determine your destination. In that case, you may be interested in all possible ways to answer your question, and depending on the searches done in the past, different types of travel, such as train or flight, can be realized. This piece of information has a significant impact on your decisions.Amazon and Netflix are working hard to personalize marketing to provide the best experience to their customers. Machine learning is a way to automate the personalization process using all available data from one customer and all customers to serve other customers.Personalization aims to tailor the process to each individual, and a machine learning model can accelerate and optimize this process by improving its model for each characteristic.Speech recognitionArtificial intelligence algorithms have many new applications in the automotive industry to assist passengers and drivers in using multimedia or navigation or other areas such as perception and behavior planning. Passengers or drivers like to take advantage of personalization and enjoy automating their habits and knowing the experiences of other passengers or drivers in the same situation.One of the most basic personalization applications in the car is recognizing the driver’s speech as the owner or regular user or passengers who use the car regularly. Providing special features or desires of a specific driver or user makes travel more enjoyable and saves time and money. If all user information is available on each car, this possibility can be extended to all users. This means that everyone has a secure key to enable speech recognition that is accessible everywhere.Personalization in servicesArtificial intelligence can recommend products or services to customers based on customer profiles. All available and real-time updated customer profiles can help provide services or products that are very useful to customers. For example, you have added new goods to your shopping list, and while you are driving in the city, AI can keep you informed of the nearest stores with the best-priced goods on your list. If AI is aware of your needs and interests, there are many personalization applications for AI. This kind of support is usually what a good friend can do for us, or at least you need time to search and find information about your needs.Data privacy is an issue that can hinder the use of artificial intelligence as your assistant in the use of any technology. Knowing more about us by AI can be a problem because we do not know who can access our data. However, as more and more applications change our lives, we will likely accept AI access to some of our private data in some way, provided the benefits to us are significant.Risk analysis and assessmentAutonomous driving requires analyzing large amounts of data and predicting and deciding a proper behavior even better than a human driver. For this reason, the safety of autonomous vehicles is still a critical factor in this technology and will determine whether the technology is mature enough to be launched or not.Safety experts are aware of this sensitivity and are looking for new solutions. Hazard analysis cannot be performed, as we did before, at design time, as a large amount of uncertainty is in front of an autonomous vehicle. Risks must be analyzed and mitigated at runtime. Personalization could simplify this solution for Level 3 autonomy, where the driver and vehicle share the responsibility for driving. Artificial intelligence algorithms collect information about drivers’ behavior and reactions while driving, and classify drivers and understand which driving tasks should be most controlled. Algorithms can check whether the driver needs to pay more attention to his driving in certain situations.E2E predictive maintenanceCar troubleshooting allows you to track the car in real-time, identify breakdowns and notify the driver to take the necessary action. For this purpose, a large amount of vehicle data must be stored and analyzed by the AI ​​algorithm. The vehicle informs the driver about possible misbehavior of hardware or software and the driver’s actions to resolve the situation.To provide end-to-end (E2E) predictive maintenance, AI prediction algorithms require large amounts of data from the vehicle, the owner, and how the owner uses the car. This application is where AI comes in to personalize customers and analyze each vehicle individually. Exchanging real-time experience between customers for a common problem can be another application based on the analysis of individual vehicles. Some applications are not supported and even driven by vehicle manufacturers, but they can be very reasonable and attractive from the customer’s point of view.Summing up,Artificial intelligence plays an important role in the automotive industry, from control responsibilities to perform driving tasks to achieve Level 4 and 5 autonomy to applications such as monitoring the driver’s behavior and eyes to ensure that the driver is ready to take over the driving task from AI in level 3 autonomy. Personalization of AI will be an essential part of this transformation, and it is our job to decide for which responsibilities we will use the benefits of AI and which we will not. Unfortunately, this question is not easy to answer because many of the technological, ethical, safety, and data security aspects of data need to be explored to find the best solutions.

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AI and financial processes: Balancing risk and reward

Juggling money

Of all the enterprise functions influenced by AI these days, perhaps none is more consequential than AI and financial processes. People don’t like when other people fiddle with their money, let alone an emotionless robot. But as it usually goes with first impressions, AI is winning converts in monetary circles, in no small part due to its ability to drive out inefficiencies and capitalize on hidden opportunities – basically creating more wealth out of existing wealth.

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A Tale of Two Cities: Modeling neighborhoods in London and Paris

London and Paris are quite a popular tourist and vacation destinations for people all around the world. They are diverse and multicultural and offer a wide variety of experiences that are widely sought after. We try to group the neighbourhoods of London and Paris respectively and draw insights to what they look like now.

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[Paper Summary] Facebook AI Releases ‘BlenderBot 2.0’: An Open Source Chatbot that searches the internet to engage in Intelligent Conversations


The GPT-3 and BlenderBot 1.0 models are extremely forgetful, but that’s not the worst of it! They’re also known to “hallucinate” knowledge when asked a question they can’t answer.

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Visualping raises $6M to make its website change monitoring service smarter


Visualping, a service that can help you monitor websites for changes like price drops or other updates, announced that it has raised a $6 million extension to the $2 million seed round it announced earlier this year. The round was led by Seattle-based FUSE Ventures, a relatively new firm with investors who spun out of Ignition Partners last year. Prior investors Mistral Venture Partners and N49P also participated.

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Netradyne raises $150 million in series C Funding Led by SoftBank Vision Fund 2


Netradyne, a leader in artificial intelligence (AI) and edge computing focusing on driver and fleet safety, announced a $150 million, Series C funding led by SoftBank Vision Fund 2, with participation from existing investors Point72 Ventures and M12. This financing, along with earlier investments, brings Netradyne’s total capital raised to over $197 million. Netradyne will use the new funding to advance its core technology, expand into new geographies and accelerate hiring across R&D, marketing, and customer support.

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Hacking your mind with AI: The case of addictive TikTok


According to the Hootsuite Digital report for 2021, with 689 million active users worldwide as of January 2021, TikTok is the 7th most popular social network in the world. TikTok’s audience is projected to grow to 1.2 billion monthly active users in 2021!

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[Paper Summary] IBM Researchers propose a Quantum Kernel Algorithm

Many quantum machine learning algorithms have been believed to provide exponential speed-ups over classical machine learning (ML) approaches, based on the assumption that classical data can be provided to the algorithm in the form of quantum states. Yet, no studies show whether a method exists that can efficiently provide data in this manner.

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Taste intelligence startup Halla closes $4.5M Series A1 to predict which grocery items shoppers will buy

Halla IO

Halla wants to answer the question of how people decide what to eat, and now has $4.5 million in fresh Series A1 capital from Food Retail Ventures to do it.

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Nym gets $6M for its anonymous overlay mixnet to sell privacy as a service

NYM Technologies

Switzerland-based privacy startup Nym Technologies has raised $6 million, which is being loosely pegged as a Series A round. Earlier raises included a $2.5M seed round in 2019. The founders also took in grant money from the European Union’s Horizon 2020 research fund during an earlier R&D phase developing the network tech.

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[Podcast] Why AI & ML Engineers should incorporate Value Sensitive Design into their Models

Today, many people realize that the shifting paradigms of AI, automation, and digital transformation will disrupt numerous human-involved processes, but few ponder how those disruptions will affect ethics and equity and principles of justice. Fewer still contemplate how to address these technoethical challenges, and what framework should be applied in doing so. Enter Steven Umbrello, Managing Director at the Institute for Ethics and Emerging Technologies.

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