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Improving Verifiability in AI Development

We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to

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We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes.

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While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization’s AI systems reflect those principles in practice. This ambiguity makes it harder for stakeholders such as users, policymakers, and civil society to scrutinize AI developers’ claims about properties of AI systems and could fuel competitive corner-cutting, increasing social risks and harms. The report describes existing and potential mechanisms that can help stakeholders grapple with questions like:

  • Can I (as a user) verify the claims made about the level of privacy protection guaranteed by a new AI system I’d like to use for machine translation of sensitive documents?
  • Can I (as a regulator) trace the steps that led to an accident caused by an autonomous vehicle? Against what standards should an autonomous vehicle company’s safety claims be compared?
  • Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?
  • Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?

The 10 mechanisms highlighted in the report are listed below, along with recommendations aimed at advancing each one. (See the report for discussion of how these mechanisms support verifiable claims as well as relevant caveats about our findings.)

Institutional Mechanisms and Recommendations

  1. Third party auditing. A coalition of stakeholders should create a task force to research options for conducting and funding third party auditing of AI systems.
  2. Red teaming exercises. Organizations developing AI should run red teaming exercises to explore risks associated with systems they develop, and should share best practices and tools.
  3. Bias and safety bounties. AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.
  4. Sharing of AI incidents. AI developers should share more information about AI incidents, including through collaborative channels.

Software Mechanisms and Recommendations

  1. Audit trails. Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.
  2. Interpretability. Organizations developing AI and funding bodies should support research into the interpretability of AI systems, with a focus on supporting risk assessment and auditing.
  3. Privacy-preserving machine learning. AI developers should develop, share, and use suites of tools for privacy-preserving machine learning that include measures of performance against common standards.

Hardware Mechanisms and Recommendations

  1. Secure hardware for machine learning. Industry and academia should work together to develop hardware security features for AI accelerators or otherwise establish best practices for the use of secure hardware (including secure enclaves on commodity hardware) in machine learning contexts.
  2. High-precision compute measurement. One or more AI labs should estimate the computing power involved in a single project in great detail and report on lessons learned regarding the potential for wider adoption of such methods.
  3. Compute support for academia. Government funding bodies should substantially increase funding for computing power resources for researchers in academia, in order to improve the ability of those researchers to verify claims made by industry.

We and our co-authors will be doing further research on these mechanisms and OpenAI will be looking to adopt several of these mechanisms in the future. We hope that this report inspires meaningful dialogue, and we are eager to discuss additional institutional, software, and hardware mechanisms that could be useful in enabling trustworthy AI development. We encourage anyone interested in collaborating on these issues to connect with the corresponding authors and visit the report website.

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Report Authors

(Equal contribution)

  • Gillian Hadfield OpenAI, University of Toronto, Schwartz Reisman Institute for Technology and Society
  • Heidy Khlaaf Adelard
  • Jingying Yang Partnership on AI
  • Helen Toner Center for Security and Emerging Technology
  • Ruth Fong University of Oxford
  • Tegan Maharaj Mila, Montreal Polytechnic
  • Pang Wei Koh Stanford University
  • Sara Hooker Google Brain
  • Jade Leung Future of Humanity Institute
  • Andrew Trask University of Oxford
  • Emma Bluemke University of Oxford
  • Jonathan Lebensold Mila, McGill University
  • Cullen O’Keefe OpenAI
  • Mark Koren Stanford Centre for AI Safety
  • Théo Ryffel École Normale Supérieure (Paris)
  • JB Rubinovitz Remedy.AI
  • Tamay Besiroglu University of Cambridge
  • Federica Carugati Center for Advanced Study in the Behavioral Sciences
  • Jack Clark OpenAI
  • Peter Eckersley Partnership on AI
  • Sarah de Haas Google Research
  • Maritza Johnson Google Research
  • Ben Laurie Google Research
  • Alex Ingerman Google Research
  • Igor Krawczuk École Polytechnique Fédérale de Lausanne
  • Amanda Askell OpenAI
  • Rosario Cammarota Intel
  • Andrew Lohn RAND Corporation
  • David Krueger Mila, Montreal Polytechnic
  • Charlotte Stix Eindhoven University of Technology
  • Peter Henderson Stanford University
  • Logan Graham University of Oxford
  • Carina Prunkl Future of Humanity Institute
  • Bianca Martin OpenAI
  • Elizabeth Seger University of Cambridge
  • Noa Zilberman University of Oxford
  • Seán Ó hÉigeartaigh Leverhulme Centre for the Future of Intelligence, Centre for the Study of Existential Risk
  • Frens Kroeger Coventry University
  • Girish Sastry OpenAI
  • Rebecca Kagan Center for Security and Emerging Technology
  • Adrian Weller University of Cambridge, Alan Turing Institute
  • Brian Tse Future of Humanity Institute, Partnership on AI
  • Elizabeth Barnes OpenAI
  • Allan Dafoe Future of Humanity Institute
  • Paul Scharre Center for a New American Security
  • Ariel Herbert-Voss OpenAI
  • Martijn Rasser Center for a New American Security
  • Shagun Sodhani Mila, University of Montreal
  • Carrick Flynn Center for Security and Emerging Technology
  • Thomas Gilbert University of California, Berkeley
  • Lisa Dyer Partnership on AI
  • Saif Khan Center for Security and Emerging Technology
  • Yoshua Bengio Mila, University of Montreal
  • Markus Anderljung Future of Humanity Institute

(Descending contribution)

Source: https://openai.com/blog/improving-verifiability/

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A Quick Guide to Conversational AI And It’s Working Process

Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, […]

The post A Quick Guide to Conversational AI And It’s Working Process appeared first on Quytech Blog.

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Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, businesses hire professionals who work round the clock to deliver support services. No matter how efficiently a business handles this segment, they might have to face problems such as “delay in responding customers’ queries” or “making a customer wait to connect with the support professionals”, and more.

A Conversational AI is a perfect solution to this most common challenge that manufacturing, FMCG, retail, e-commerce, and other industries are facing. Never heard of this term?

Well, this is the latest trend in almost all the industries that are already using artificial intelligence technology or wanting to adopt the same in their business operations. Let’s read about the same in detail.

What is Conversational AI?

Conversational AI is a specific kind of artificial intelligence that makes software interact and converse with users in a highly intuitive way. It uses natural language processing (NLP) to understand and interact in human language.

The conversational AI, when integrated into chatbots, messengers, and voice assistants, enables businesses to deliver personalized customer experience and achieve 100% customer satisfaction. Google Home and Amazon Echo are the two popular examples of it.

Applications of Conversational AI

Conversational AI a new and automated way of offering customer support services. Healthcare, Adtech, logistics, insurance, travel, hospitality, finances, and other industries are using technology in the following:

Messaging applications

Conversational AI can be used in a messaging application to offer personalized support services through chat. Your customers can choose the “chat support” option and talk to the chatbot to get the support.

Speech-based virtual assistants

A conversational AI can use speech recognition technology so that you can offer a speech-based virtual assistant for your customers. These are the type of chatbots where users can get any information through voice commands.

Virtual customer assistants

This type of conversational AI helps in offering online support to customers; you can develop the same to offer support through Web, SMS, messaging applications, and more.

Virtual personal assistants

A virtual personal assistant, powered by conversational AI, minimizes the need of hiring a huge team to offer dedicated support services to each of your customers.

Virtual employee assistants

Employees working in big organizations might need various types of assistants. A conversational AI used to build a virtual employee assistant can be the point of contact for all such employees. They can find the required information just by interacting with that assistant.

Robotic process automation

Robotic process automation using the potential of conversational AI helps a machine to understand human conversations and their intent to perform automated tasks.

Working of conversational AI

Machine learning, deep learning, and natural language processing are the three main technologies behind conversational AI. Here is how it works:

  1. Collection of unstructured data from various sources
  2. Data preprocessing and feature engineering
  3. Creating an AI model
  4. Training the model to automatically improve from experiences
  5. Testing the model
  6. Detecting patterns and making decisions
  7. AI deployment

What benefits businesses can get by using conversational AI?

Apart from helping businesses to deliver an unmatched customer experience, conversational AI can offer a plethora of other benefits that include:

Saves Time

Having an automated chatbot would help you save a considerable amount of time. The saved time can be used to perform other tasks or focus on your business’ marketing and promotion.

Helps in providing Real-Time Support Services

A conversational AI can handle multiple queries at one time, without even letting other customers wait. In short, every customer will feel like they are getting dedicated support services in real-time.

Improves business efficiency

With a conversational AI, you can have the assurance that your customers are being taken care of properly. In short, their queries are being handled and resolved immediately. You can focus on other segments of your business and improve efficiency.

Helps in lowering down Customers’ Complaints

Since a conversational AI can immediately respond to queries of the customers, it can help to reduce the number of complaints. Resolving customers’ complaints without making them call a support professional would increase customer loyalty and increase your brand reputation.

Increases chances of sales

By providing a persistent communication channel that precedes the context further, you can make your customers explore more and shop more. Moreover, a conversational AI can also help in reducing the cart abandonment rate as it can provide immediate assistance regarding the issues a user is facing while making the payment, applying a discount code, or at any other time.

The user would not even have to contact the support center. To understand this better, let’s take an example- a user has added one or more products to the cart, but he/she is unable to find his/her preferred mode of payment. After a few minutes, what would the customer do, either visit the contact us section to get the customer support number and connect to a professional (which is a time taking process) or simply get the support through a conversational AI. The latter would automatically send a message to the customer to ask the query and provide a resolution.

Now when you know everything about conversational AI and want to build one for you, then contact Quytech, a trusted AI app development company. Quytech has more than a decade of experience in working on artificial intelligence, machine learning, and other latest technologies.

Final Words

Are you curious to know about conversational artificial intelligence? Give this article a read to know the definition and working of conversational AI in detail. We have also mentioned the reasons why this technology is becoming the talk of the town among businesses of all sizes and types. After reading the article, if you want to develop a tailor-made conversational AI for your business, then reach out to a reliable and experienced AI development company or hire AI developers with considerable experience.

Source: https://www.quytech.com/blog/what-is-conversational-ai-and-how-does-it-work/

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Are Legal chatbots worth the time and effort?

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KLoBot — the Best AI-Chatbot builder platform

The legal industry is always known for its resistance to change, but technology in the legal landscape has seen rapid growth from the past few years. The global Coronavirus pandemic has also accelerated the pace of investments in legal technology, which is likely to transform the legal marketplace.

Several law firms are majorly focusing on the adoption of innovative technology, which has the capability to modernize the practice of law. Innovative technologies, including Artificial Intelligence, Analytics, and Blockchain, among others, prioritizes the speed and efficiency of legal services.

Technology in the legal sector is an enabler that empowers attorneys and paralegals to perform their jobs better.

The use of AI and its applications in the legal industry is moving higher and is becoming the next big thing for legal firms. By 2024 the legal AI software market is expected to reach $1,236 million and is forecasted to grow at a CAGR of 31.3% during 2019–2024. (2)

Incorporating AI into legal practice can augment the workflows and streamline the work processes. AI-powered chatbots are disrupting the legal industry and are poised to become a preferred mode of communication for internal as well as external users. Leveraging NLP and NLU algorithm power, which are one of the prominent fields in AI, chatbots can understand intents, contexts, and further handle end to end human-machine interactions.

Law firms, as well as corporate legal departments, continue to look for new ways to enhance efficiency and drive productivity. AI-enabled chatbots are one such way that has the potential to revolutionize the law firm operations. These chatbots are a new approach for law firms to imitate human conversations and automatically respond to clients as well as attorneys’ queries.

Legal chatbots have the capabilities to make better and quicker decisions when compared to human agents. It reduces the burden on attorneys and paralegals to repetitively answer the same queries, which further brings consistency to users’ responses.

1. 8 Proven Ways to Use Chatbots for Marketing (with Real Examples)

2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Internal Chatbots

Internal chatbots are nothing but the chatbots for internal operations and communications, helping law firms manage enterprise collaboration. Internal legal chatbots help law firms automate the contract review process, which is one of the most tedious tasks for attorneys and in-house counsel.

Legal chatbots for attorneys come with a predefined set of policies to review & analyze documents, perform due diligence, and automate other monotonous tasks that attorneys perform. Other basic tasks comprising scheduling meetings, setting up reminders, and searching relevant matter information can also be performed by legal chatbots. Internal legal chatbots empower attorneys to reduce the risk of human errors by automating the monotonous administrative chores and allow them to focus more on higher value and complicated tasks that need attorney’s intervention.

External Chatbots

External chatbots, on the other side, are the client-facing legal chatbots. These chatbots can draft the legal documents, including UCC filings, divorce forms, and other non-disclosure agreements based on the client inputs.

External legal chatbots empower law firms to handle the client intake process efficiently and generate leads, which further reduces an attorney’s time spent on these activities.

In the current scenario of receiving information instantly at a fingertip, legal chatbots serve as the best solution to handle client queries and provide legal advice.

External, as well as internal legal chatbots with their 24/7 supporting abilities, facilitate law firms to manage operational costs and meet the evolving client expectations.

Although chatbots are taking time to augment legal services but are worth the effort.

KLoBot is an incredibly intelligent AI chatbot builder platform that allows legal firms to create text and voice-based chatbots within minutes. KLoBot’s easy drag and drop skill interface helps law firms to design no-code chatbots that can be deployed across an organization’s favorite channels. The chatbots built on the KLoBot platform help law firms perform simple and complex routine tasks, including QnA and knowledge repository search. Few other jobs, including scheduling meetings, setting up reminders, completing actions on behalf of attorneys, finding colleagues, assisting attorneys, and much more, are also being performed by KLoBot enabled chatbots.

KLoBot enabled chatbots to act as a personal assistant and enhance attorneys as well as client experiences. These chatbots empower law firms to simplify internal as well as external communications and streamline business processes.

KLoBot AI chatbots with its feature-rich admin console, provide law firms robust security controls. To know more about KLoBot click here.

References

[1] The Law Society Capturing Technological Innovation in Legal Services Report

[2] https://www.marketsandmarkets.com/

Source: https://chatbotslife.com/are-legal-chatbots-worth-the-time-and-effort-5f44936f7e89?source=rss—-a49517e4c30b—4

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Things to Know about Free Form Templates

A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to […]

The post Things to Know about Free Form Templates appeared first on 1redDrop.

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A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to view and also work with the form files. 

It will create a new free form template by copying and storing those files within a folder. A form template (.XSN) file designing or creation of a single file will include various supporting files. Users may fill out the online form by accessing the .XML form file, which is a form template.

Designing Free Form Templates

There are numerous processes that define free form template design, and are as follows:

  • Designing the form’s appearance – the instructional text, labels, and controls
  • Controls will assist with user interaction behavior on the form template. You can design a specific section to appear or disappear when the user chooses a particular option
  • Whether the form template may include some additional views. For a permit application form design, for example, you have to provide different views for each person. One view especially for the electrical contractor, next for the receiving agent, and finally, the investigator. He or she will deny or approve the permit application
  • Next, you need to know how & where to store the form data. Designing free from templates will allow users to submit their data within the database either online or direct access. If not, they can also store the same in any specific shared folder
  • It is essential to design the other elements, colors, and fonts within the form template
  • Users must be able to personalize the form. Allowing users to include various rows within the optional section, repeating section, or a repeating table
  • Users should receive a notification when they forget to input a mandatory field or make mistakes within the form
  • After completing the free form templates design, you can publish the same online using a .XSN file format

Club Signup Form

A simple registration form can help your Club Signup Form creation process go smoother. This signup form could be an ideal solution for a new club membership registration for any organization or club.

Application Form

Application form templates are much easier to use & set-up to streamline your application process. You can customize this online form and utilize the same for numerous applications. Make use of this application form as a job application form, volunteer applications, contest entries, or high school scholarship applications. It is an ideal solution for scholarship programs, nonprofit organizations, business owners, and many such users and use cases.

Scheduling Form

Scheduling form templates are handy and can be used for numerous appointment booking requirements. A scheduling form is also utilized for various appointment scheduling or online reservations and booking purposes. Regardless of your business requirement, it is easy to customize the form template.

Concept Testing Survey

While testing a new design or concept, it is essential to gather the responses quickly. Freeform templates for a concept testing survey make it much easier to gather product feedback and reach the target audience. It is essential to conduct market research while planning to release a new product. A mobile-friendly form will allow you to utilize the survey questions for collecting the product’s consumer input quickly.

Credit Card Order Form

It is not always a complex process to provide an online credit card payment form for the customers. This form template will allow you to access numerous services or products for collecting card payment information. You can utilize this yet-another endless and simple payment form.

Employment Application Form

The employment application form for recruitment will assist the HR team to gather the required information from candidates. During the interview or application process, you can easily remove any expensive follow-ups. Some of the fields are contact information, employment history, useful information, etc. as well as an outline of the job description, consent for background checks, military service record, anticipated start date, any special skills, and many more. It is optional to enable notifications for the form owners to receive an alert or email when a new employment application is submitted.

Source: https://1reddrop.com/2020/10/24/things-to-know-about-free-form-templates/?utm_source=rss&utm_medium=rss&utm_campaign=things-to-know-about-free-form-templates

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