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What Is the Difference Between a Chatbot and a Virtual Assistant?

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They will soon become a cornerstone of our daily lives but what exactly is the difference between a chatbot and a virtual assistant?

Summary:

The history of technological development is littered with examples of various formats fighting it out for market dominance. The VHS and the Betamax, the Blu-ray and HD DVD, or more recently the current virtual headset battle between HTC Vive and Oculus Rift. At times, these format wars will dictate what we refer to the new invention as. When purchasing a high-density optical disc we tend to ask for a Blu-ray for example.

As artificial intelligence moves out of its winter, we are encountering confusion over what to call the intelligent computer programs that communicate with us. Chatbot or virtual assistant?

Are chatbots and virtual assistants the same?

It depends on who you speak to. A school of thought exists which believes there is no difference and that either one could be an umbrella term for the conversational agent.

If this is the case then it seems redundant to have two names for the same function. Chatbot is by far the more popular term according to Google Trends.

In general, if its primary mode of interaction is through messaging (Slack, Facebook etc.) then you are communicating with a chatbot. There is an argument that the likes of Siri cannot be a chatbot because it exists outside of these channels. But this does not feel like enough of a differentiator.

In fact, of more importance is the function of the chatbot (or virtual assistant) that you employ. In this regard, there are some myths surrounding their capabilities that should be debunked.

Myth 1: A chatbot is not intelligent enough

Some of the most powerful chatbots are equipped with robust natural language processing in order to understand the meaning of an inquiry rather than simply the keywords.

Previous bots might have only been able to carry out a limited number of conversations through either hard-coding, wildcard matching of words and phrases or time-consuming keyword training. However, bots powered with NLP are now far more flexible. Unfortunately, many chatbots do not leverage true NLP and are giving chatbots a bad name.

Thanks to machine learning, chatbots will continue to improve and will produce higher self-service rates than ever before.

Myth 2: A virtual assistant can carry out a wider range of functions

While there might be some truth to this now, the gap between what the two hope to achieve is constantly narrowing.

In the past, the chatbot could only perform specific tasks, such as a password change or information about the weather. Whereas, the virtual assistant was more wide-ranging in what it offered.

Thanks to advancements in NLP and machine learning, however, this is changing. Chatbots are now far more diverse and can carry out more functions through their ability to understand natural language. The use of decision trees, for example, makes it far easier to discover the exact intent behind user inquiries, broadening its functionality even further.

Myth 3: A virtual assistant is better at remembering the context

Even now, virtual assistants still struggle to remember key information during conversations but chatbots are already proving they can store what you tell them.

For example, Inbenta’s chatbot Veronica is able to remember your email address if you provide it to her.

If you tell her “My email address is….” then she will retain that information for future use. Therefore, if you were to ask for a demo she would not require you to resubmit it.

Rather than debate what we should name them, it is important to recognize how the chatbot (or virtual assistant) will provide the most human-like experience possible by understanding our natural language to the best capabilities.

Myth 4: Chatbots can’t remember previous interactions with users

One of the most spread myths about chatbots is that they aren’t able to recall previous interactions with a user. A few years ago, that was true. However, nowadays, with the use of AI, chatbots collect information from the user, and not only can they refer to previous conversations, but they can also act accordingly.

Let’s say a user has made a purchase on an e-commerce site that uses an AI chatbot. That information is stored on the user’s profile and is accessible by the bot. This allows the chatbot to easily recommend similar or related products in a proactive way, or respond to requests of information regarding orders and track their delivery for the customers.

Myth 5: Chatbots can’t identify sentiment

Before, businesses would turn to virtual assistants for those applications where more human or emotional intelligence was needed. However, this is largely obsolete. Modern-day chatbots not only detect sentiment, but also learn with new interactions with visitors and customers, broadening the empathetic responses they can provide to reassure visitors whenever they have doubts or get angry due to transaction issues.

Myth 6: Keeping consistency across channels is hard with a chatbot

The implementation of chatbot instances use to be confusing, as support requests weren’t centralized and every channel instance of a chatbot require its own platform and responses. This created the idea that chatbots couldn’t keep consistency. Another issue is the common use of machine learning techniques, where chatbots learn by themselves without human supervision. The lack of human control over them can create patterns the lead bots to deliver different responses to the same questions depending on the user, the time of the date or hundreds of other variables.

All these scenarios together have damaged the image of chatbots in terms of consistency.

However, the use of Symbolic AI and NLP techniques, together with new platforms providing cross and omnichannel support have eliminated this issue and improved the way large organizations empower their customer service teams with chatbot solutions.

A Final Note on virtual assistant and chatbot

As both chatbot and virtual assistant technology improves, the lines between both of them become blurry and more difficult to define. It is likely that, in the years to come, both technologies will be assimilated into one and the names will be interchangeable.

Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce, and conversational chatbots providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue.

Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company.

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Source: https://www.inbenta.com/en/blog/difference-chatbot-virtual-assistant/

AI

10 steps to educate your company on AI fairness

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Elevate your enterprise data technology and strategy at Transform 2021.


As companies increasingly apply artificial intelligence, they must address concerns about trust.

Here are 10 practical interventions for companies to employ to ensure AI fairness. They include creating an AI fairness charter and implementing training and testing.

Data-driven technologies and artificial intelligence (AI) are powering our world today — from predicting where the next COVID-19 variant will arise, to helping us travel on the most efficient route. In many domains, the general public has a high amount of trust that the algorithms that are powering these experiences are being developed in a fair manner.

However, this trust can be easily broken. For example, consider recruiting software that, due to unrepresentative training data, penalizes applications that contain the word “women”, or a credit-scoring system that misses real-world evidence of credit-worthiness and thus as a result certain groups get lower credit limits or are denied loans.

The reality is that the technology is moving faster than the education and training on AI fairness. The people who train, develop, implement and market these data-driven experiences are often unaware of the second or third-order implications of their hard work.

As part of the World Economic Forum’s Global Future Council on Artificial Intelligence for Humanity, a collective of AI practitioners, researchers and corporate advisors, we propose 10 practical interventions for companies to employ to ensure AI fairness.

1. Assign responsibility for AI education

Assign a chief AI ethics officer (CAIO) who along with a cross-functional ethics board (including representatives from data science, regulatory, public relations, communications and HR) should be responsible for the designing and implementing AI education activities. The CAIO should also be the “ombudsman” for staff to reach out to in case of fairness concerns, as well as a spokesperson to non-technical staff. Ideally this role should report directly to the CEO for visibility and implementation.

2. Define fairness for your organization

Develop an AI fairness charter template and then ask all departments that are actively using AI to complete it in their context. This is particularly relevant for business line managers and product and service owners.

3. Ensure AI fairness along the supply chain

Require suppliers you are using who have AI built into their procured products and services – for instance a recruiting agency who might use AI for candidate screening – to also complete an AI fairness charter and to adhere to company policies on AI fairness. This is particularly relevant for the procurement function and for suppliers.

4. Educate staff and stakeholders through training and a “learn by doing” approach

Require mandatory training and certification for all employees on AI fairness principles – similar to how staff are required to sign up to codes of business conduct. For technical staff, provide training on how to build models that do not violate fairness principles. All trainings should leverage the insights from the AI fairness charters to directly address issues facing the company. Ensure the course content is regularly reviewed by the ethics board.

5. Create an HR AI fairness people plan

An HR AI fairness plan should include a yearly review by HR to assess the diversity of the team working on data-driven technologies and AI, and an explicit review and upgrade of the competencies and skills that are currently advertised for key AI-relevant product development roles (such as product owner, data scientist and data engineer) to ensure awareness of fairness is part of the job description.

6. Test AI fairness before any tech launches

Require departments and suppliers to run and internally publish fairness outcomes tests before any AI algorithm is allowed to go live. Once you know what groups may be unfairly treated due to data bias, simulate users from that group and monitor the results. This can be used by product teams to iterate and improve their product or service before it goes live. Open source tools, such as Microsoft Fairlearn, can help provide the analysis for a fairness outcome test.

7. Communicate your approach to AI fairness

Set up fairness outcomes learning sessions with customer- and public-facing staff to go through the fairness outcomes tests for any new or updated product or service. This is particularly relevant for marketing and external communications, as well as customer service teams.

8. Dedicate a standing item in board meetings to AI fairness processes

This discussion should include the reporting on progress and adherence, themes raised from the chief AI ethics officer and ethics board, and the results of high-priority fairness outcomes tests

9. Make sure the education sticks

Regularly track and report participation and completion of the AI fairness activities, along with the demonstrated impact of managing fairness in terms of real business value. Provide these updates to department and line managers to communicate to staff to reinforce that by making AI platforms and software more fair, the organization is more effective and productive.

10. Document everything

Document your approach to AI fairness and communicate it in staff and supplier trainings and high-profile events, including for customers and investors.

[This story originally appeared on 10 steps to educate your company on AI fairness | World Economic Forum (weforum.org). Copyright 2021.]

Nadjia Yousif is Managing Director and Partner at Boston Consulting Group and co-leads the Financial Institutions practice for the UK the Netherlands and Belgium.

Mark Minevich is Chair for Artificial Intelligence Policy at the International Research Centre on Artificial Intelligence under the auspices of UNESCO, Jozef Stefan Institute.

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Source: https://venturebeat.com/2021/06/11/10-steps-to-educate-your-company-on-ai-fairness/

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Artificial Intelligence

The rise of robotaxis in China

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AutoX, Momenta and WeRide took the stage at TC Sessions: Mobility 2021 to discuss the state of robotaxi startups in China and their relationships with local governments in the country.

They also talked about overseas expansion — a common trajectory for China’s top autonomous vehicle startups — and shed light on the challenges and opportunities for foreign AV companies eyeing the massive Chinese market.


Enterprising governments

Worldwide, regulations play a great role in the development of autonomous vehicles. In China, policymaking for autonomous driving is driven from the bottom up rather than a top-down effort by the central government, observed executives from the three Chinese robotaxi startups.

Huan Sun, Europe general manager at Momenta, which is backed by the government of Suzhou, a city near Shanghai, said her company had a “very good experience” working with the municipal governments across multiple cities.

In China, each local government is incentivized to really act like entrepreneurs like us. They are very progressive in developing the local economy… What we feel is that autonomous driving technology can greatly improve and upgrade the [local governments’] economic structure. (Time stamp: 02:56)

Shenzhen, a special economic zone with considerable lawmaking autonomy, is just as progressive in propelling autonomous driving forward, said Jewel Li, chief operation officer at AutoX, which is based in the southern city.

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Source: https://techcrunch.com/2021/06/11/the-rise-of-robotaxis-in-china/

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Can we afford AI?

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Of all the concerns surrounding artificial intelligence these days — and no, I don’t mean evil robot overlords, but more mundane things like job replacement and security — perhaps none is more overlooked than cost.

This is understandable, considering AI has the potential to lower the cost of doing business in so many ways. But AI is not only expensive to acquire and deploy, it also requires a substantial amount of compute power, storage, and energy to produce worthwhile returns.

Back in 2019, AI pioneer Elliot Turner estimated that training the XLNet natural language system could cost upwards of $245,000 – roughly 512 TPUs running at full capacity for 60 straight hours. And there is no guarantee it will produce usable results. Even a simple task like training an intelligent machine to solve a Rubik’s Cube could draw up to 2.8GW of power, about the hourly output of three nuclear power plants. These are serious — although still debatable — numbers, considering that some estimates claim technology processes will draw more than half of our global energy output by 2030.

Silicon solution

Perhaps no one understands this better than IBM, which has been at the forefront of the AI evolution — with varying degrees of success –thanks to platforms like Watson and Project Debater. The company’s Albany, New York-based research lab has an AI Hardware Center that might be on the verge of unveiling some intriguing results in the drive to reduce the computational demands of training AI and guiding its decision-making processes, according to Tirias Research analyst Kevin Krewell.

A key development is a quad-core test chip recently unveiled at the International Solid-State Circuits Conference (ISSCC). The chip features a hybrid 8-bit floating-point format for training functions and both 2- and 4-bit integer formats for inference, Krewell wrote in a Forbes piece. This would be a significant improvement over the 32-bit floating-point solutions that power current AI solutions, but only if the right software can be developed to produce the same or better results under these lower logic and memory footprints. So far, IBM has been silent on how it intends to do this, although the company has announced that its DEEPTOOLS compiler, which supports AI model development and training, is compatible with the 7nm die.

Qualcomm is also interested in driving greater efficiency in AI models, with a particular focus on Neural Architecture Search (NAS), the means by which intelligent machines map the most efficient network topologies to accomplish a given task. But since Qualcomm’s chips generally have a low power footprint to begin with, its focus is on developing new, more efficient models that work comfortably within existing architectures, even at scale.

All for one

To that end, the company says it has adopted a holistic approach to modeling that stresses the need to shrink multiple axes — like quantization, compression, and compilation — in a coordinated fashion. Since all of these techniques complement each other, researchers must address the efficiency challenge from their unique angle but not so that a change in one area disrupts gains in another.

When applied to NAS, the key challenges are reducing high compute costs, improving scalability, and delivering more accurate hardware performance metrics. Called DONNA (Distilling Optimal Neural Network Architectures), the solution provides a highly scalable means to define network architectures around accuracy, latency, and other requirements and then deploy them in real-world environments. The company is already reporting a 20% speed boost over MobileNetV2 in locating highly accurate architectures on a Samsung S21 smartphone.

Facebook also has a strong interest in fostering greater efficiency in AI. The company recently unveiled a new algorithm called Seer (SElf-supERvised) that reduces the amount of labeling required to make effective use of datasets. The process allows AI to draw accurate conclusions using a smaller set of comparative data. In this way, it can identify, say, a picture of a cat without having to comb through thousands of existing pictures that have already been labeled as cats. This reduces the number of human hours required in training, as well as the overall data footprint required for identification, all of which speeds up the process and lowers overall costs.

Speed, efficiency, and reduced resource consumption have been driving factors in IT for decades, so it’s no surprise that these goals are starting to drive AI development as well. What is surprising is the speed at which this is happening. Traditionally, new technologies are deployed first, leaving things like costs and efficiency as afterthoughts.

It’s a sign of the times that AI is already adopting streamlined architectures and operations as core capabilities before it hits a critical level of scale. Even the most well-heeled companies recognize that the computational requirements of AI are likely to be far greater than anything they’ve encountered before.

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Source: https://venturebeat.com/2021/06/11/can-we-afford-ai/

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AI Weekly: AI helps companies design physical products

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This week in a paper published in the journal Nature, researchers at Google detailed how they used AI to design the next generation of tensor processing units (TPU), the company’s application-specific integrated circuits optimized for AI workloads. While the work wasn’t novel — Google’s been refining the technique for the better part of years — it gave the clearest illustration yet of AI’s potential in hardware design. Previous experiments didn’t yield commercially viable products, only prototypes. But the Nature paper suggests AI can at the very least augment human designers to accelerate the brainstorming process.

Beyond chips, companies like U.S.- and Belgium-based Oqton are applying AI to design domains including additive manufacturing. Oqton’s platform automates CNC, metal, and polymer 3D printing and hybrid additive and subtractive workflows, like creating castable jewelry wax. It suggests a range of optimizations and fixes informed by AI inspection algorithms, as well as by pre-analyses of part geometry and real-time calibration. For example, Oqton can automatically adjust geometries to get parts within required tolerances, simulating heat treatment effects like warpage, shrinkage, and stress relief on titanium, cobalt, chrome, zirconia, and other materials.

While it’s still in the research stages, MIT’s Computer Science and Artificial Intelligence Laboratory developed an AI-powered tool called LaserFactory that can print fully functional robots and drones. LaserFactory leverages a three-ingredient recipe that lets users create structural geometry, print traces, and assemble electronic components like sensors, circuits, and actuators. As the researchers behind LaserFactory note in a paper describing their work, it could in theory be used for jobs like delivery or search-and-rescue.

At Renault, engineers are leveraging AI-powered software created by Siemens Digital Industries Software to automate the design of automated manual transmission (AMT) systems in cars. AMT, which behaves like an automatic transmission but allows drivers to shift gears electronically using a push-button, can take up to a year of trial and error to ideate, develop, and thoroughly validate. But Siemen’s tool enables Renault engineers to drag, drop, and connect icons to graphically create a model of an AMT. The software predicts the behavior and performance of the AMT’s components and makes any necessary refinements early in the development cycle.

Even Nutella is tapping AI for physical products, using the technology to pull from a database of dozens of patterns and colors to create different versions of its packaging. In 2017, working with advertising agency Ogilvy & Mather Italia, the company splashed over 7 million unique designs on “Nutella Unica” jars throughout Italy, which sold out in a month.

Philosophical shift

People might perceive these applications as taking agency away from human designers, but the coauthors of a recent Harvard Business School working paper argue that AI actually enables designers to overcome past limitations — from scale and scope to learning.

“In the context of AI factories, solutions may even be more user-centered, more creative, and continuously updated through learning iterations that span the entire life cycle of a product. Yet, we found that AI profoundly changes the practice of design,” the coauthors write. “Problem solving tasks, traditionally carried on by designers, are now automated into learning loops that operate without limitations of volume and speed. These loops think in a radically different way than a designer: they address complex problems through very simple tasks, iterated exponentially.”

In a recent blog post, user experience designer Miklos Philips echoed the findings of the Harvard Business Review paper contributors, noting that designers working with AI can create prototypes quickly and more cheaply due to the increased efficiency it offers. AI’s power will lie in the speed in which it can analyze vast amounts of data and suggest design adjustments, he says, so that a designer can cherry-pick and approve adjustments based on data and create the most effective designs to test expediently.

In any case, the ROI of AI-assisted design tools is potentially substantial. According to a 2020 PricewaterhouseCoopers survey, companies in manufacturing expect efficiency gains over the next five years attributable to digital transformations, including the adoption of AI and machine learning. Perhaps unsurprisingly, 76% of respondents to a Google Cloud report published this week said they’ve turned to “disruptive technologies” like AI, data analytics, and the cloud, particularly to help navigate challenges brought on by the pandemic.

Given the business value, AI-powered design is likely here to stay — and to grow. That’s generally good news not only for designers, but for the enterprises and consumers that stand to reap the benefits of automation across physical product creation.

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Source: https://venturebeat.com/2021/06/11/ai-weekly-ai-helps-companies-design-physical-products/

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