• In 2023, AI language models have also become more adept at handling unstructured data, such as text, audio, and video.
• As Language Model AI has become more sophisticated and powerful, there has also been increased focus on ethical considerations and responsible use of AI.

In 2023, AI language models have continued to evolve and become more sophisticated, allowing them to perform a broader range of tasks and assist humans in more complex ways.

But why do they matter so much?

Importance of AI Language Model

Language Model AI is an algorithm designed to process, understand and produce language with high accuracy and fluency. These models are becoming increasingly popular due to the numerous benefits they offer. In this article, we will explore some of its benefits of it.

• Improved language processing and comprehension: Language Model AI can process and comprehend vast amounts of language data with high accuracy. This means they can quickly analyze and understand large amounts of text and provide insights into the meaning and context of the language used.
• Increased efficiency: It can automate many language-related tasks, such as summarizing, categorizing, and translating text. This can save time and increase productivity for individuals and organizations that need to process large volumes of language data.
• Personalization: The language AI Model can be trained on specific data sets, allowing them to provide personalized language recommendations and predictions. This can be particularly useful in marketing and customer service, where personalized language can increase engagement and satisfaction.
• Language translation: AI language models can accurately translate between languages, which are especially valuable in today’s globalized world. This can facilitate communication between people who speak different languages and improve cross-cultural understanding.
• Natural language generation: AI language models can generate natural-sounding language closely mimicking human speech. This can be useful in creative writing and content generation, where natural-sounding language is essential.
• Accessibility: It can make the language more accessible for people with disabilities, such as those who are visually impaired or have difficulty with speech. For example, text-to-speech and speech-to-text systems that utilize it can facilitate communication for individuals who struggle with traditional written or spoken language.

The Advancements of the AI Language Model

One of the most significant advancements in AI language models in 2023 has been in natural language processing (NLP). NLP refers to the ability of AI systems to understand and generate human language. With improvements in NLP, Language AI Models have become more capable of understanding the nuances of human language, including idioms, sarcasm, and context. This has made Language AI Model more useful in customer service chatbots, virtual assistants, and machine translation applications.

Another significant advancement in Language Model AI in 2023 is voice recognition and synthesis. With more advanced speech recognition technology, AI language models can now understand and transcribe speech more accurately.

Additionally, with improvements in speech synthesis technology, Language Model AI can generate more natural-sounding speech, making them more useful for applications such as text-to-speech, voice assistants, and audio content creation.

In 2023, AI language models have also become more adept at handling unstructured data, such as text, audio, and video. This has led to more advanced AI applications that can analyze large amounts of unstructured data to derive insights and make predictions.

For example, AI language models can be used to analyze social media posts to understand public sentiment about a particular product or topic or to analyze customer feedback to identify areas for improvement in a business.

Generative AI is one of the most exciting developments in AI language models in 2023. Generative AI refers to AI systems that create new content, such as text, images, and videos, based on patterns and structures learned from existing data. With advances in generative AI, Language Model AI have become capable of creating highly realistic and convincing content, such as fake news articles, deepfake videos, and even entire articles that are difficult to distinguish from those written by humans.

While generative AI has many potential applications, it also poses a significant challenge in ensuring the content’s authenticity and integrity. In 2023, there has been increased attention on developing techniques to detect and mitigate the effects of generative AI-generated content, such as using metadata and watermarking to identify the source of content and developing algorithms to detect and flag fake content.

As Language Model AI has become more sophisticated and robust, there has also been increased focus on ethical considerations and responsible use of AI. In 2023, there has been a growing awareness of the potential biases and unintended consequences that can arise from using Language Model AI, particularly in areas such as hiring and decision-making. As a result, there has been increased emphasis on developing transparent and ethical AI systems that can be audited and monitored for fairness and accountability.

In conclusion, the advancements in AI language models have significantly improved natural language processing, machine learning, and artificial intelligence. The introduction of large-scale language models such as GPT-3 has revolutionized the field of language generation and automated text processing, enabling the creation of more human-like and accurate machine-generated text. This has opened up new possibilities in areas such as chatbots, virtual assistants, and automated content creation, making it possible for businesses to leverage the power of AI to improve their operations and customer experience.

Furthermore, with ongoing research and development in the field, we can expect more innovative advancements in AI language models.

• Jurassic-2, like Jurassic-1, will be offered through AI21 Studio, an NLP-as-a-Service developer platform, and will come in three sizes: Large, Grande, and Jumbo, each with a unique instruction-tuned version.
• In addition to Jurassic-2, AI21 Labs is also releasing five brand-new commercial APIs, each of which is available separately and can be used for various tasks like grammar checking and article summaries.

For businesses looking to develop sophisticated, chat-based AI applications at scale, generative artificial intelligence research lab AI21 Labs Ltd. has released Jurassic-2, which it claims is the most customizable large language model in the world.

A business and an AI research facility, Ai21 Labs provides its natural language processing capabilities as a service. In order to further this goal, the company has developed several helpful NLP applications based on Jurassic-1, its original language model that was released in 2021. The company’s mission is to change and shape how people read and write by making AI a “thought partner” to humans.

Wordtune, an AI-powered writing assistant that can comprehend content and meaning to assist people in expressing their ideas more transparently and compellingly, is one of its applications. Wordtune Read is another tool that can quickly analyze lengthy documents and produce a simplified summary. Users can read and comprehend lengthy and complex texts more rapidly and effectively.

Jurassic-1 has also been utilized by many businesses, such as the video game development platform Latitude.io, which scales the creation of its gaming worlds using generative AI, and the youth employment accelerator Harambee, which developed a custom chatbot to boost enrollment in its initiatives.

Based on Jurassic-1, AI21 Labs has also created its own chatbots. These include the amusing “Ask Ruth Bader Ginsburg” AI, which was trained on more than 27 years of public interviews, speeches, and written texts by the late U.S. Supreme Court Justice Ruth Bader Ginsburg.

The popularity of OpenAI LLC’s popular chatbot ChatGPT has recently raised awareness of generative AI, and there is great excitement about its potential applications in fields like search. Because of ChatGPT’s enormous success and close relationship with Microsoft Corp., OpenAI is widely regarded as the industry pioneer in generative AI.

However, analysts claim that AI21 Labs’ Jurassic models are a competitive alternative to the GPT-3 model that drives ChatGPT and that the company has attracted some sizeable funding of its own. For instance, Jurassic-2 performs exceptionally well when compared to other commercially available LLMs, according to a Stanford University comparison study.

Udi Karpas, Squad director at AI21 Labs Studio, told a leading media house, “Our largest model, Jurassic-2 Jumbo, is second to OpenAI’s models, with additional smaller models of ours being in the same league as DaVinci 2 and amp; 3, for example. Jurassic-2 Grande also outperforms many competitor models despite its smaller size.”

With the release of Jurassic-2, AI21 Labs is providing its clients with a more sophisticated baseline model that includes dozens of new features that should enable the development of even more advanced chatbots. For example, it is claimed to have more sophisticated instruction-following capabilities that are made possible by thorough instruction tuning on proprietary data. Additionally, it has fewer latency issues, with response times up to 30% faster than Jurassic-1, and it supports more languages with new additions like Dutch, French, German, Italian, Portuguese, and Spanish.

With Jurassic-2, AI21 Labs offered five additional APIs for commercial usage based on the new paradigm. These include Paraphrase, which may rewrite up to a whole paragraph of text in the user’s preferred manner; Summarize, which provides a comprehensive summary of long-form publications; Text Suggestions, which suggests enhancements to any given text, such as enhancing the vocabulary. Grammatical error correction, which checks for grammatical problems, and Text Segmentation, which divides lengthy texts into relevant chunks depending on their themes.

According to Karpas, although Jurassic-2 and GPT-3 are similar in many ways, Jurassic-2 excels at reading and writing tasks like text completion, text generation, and summarization. Karpas shared, “Jurassic-2 can support any application which is text-centric. Examples of products currently supported include gaming, writing assistants, product descriptions in e-commerce and more.”

AI21 Labs stated that Jurassic-2 would be made accessible as a developer platform for NLP-as-a-service through AI21 Studio in three sizes. According to the startup, developers can use Jurassic-2 to build text-based applications like chatbots, virtual assistants, tools for content moderation and text simplification, writing assistants, and more.

The company further stated that custom models would cost the same as basic ones, and Jurassic-2 will also be 10% less expensive for current customers than its predecessor.

According to Andy Thurai, vice president and principal analyst at Constellation Research Inc., Jurassic-2 stands out for a number of reasons, including the fact that even though its parameters are only marginally higher than those of rival LLMs like GPT-3, its vocabulary is five times larger, making it significantly more accurate in providing language solutions to customers. Jurassic-2 is very cost-competitive, he added, adding that.

Thurai explained, “AI21’s NLP-as-a-service developer platform will be offered in three sizes – large, grande and jumbo — providing customers with more flexibility regarding costs and capabilities, so they can choose according to their needs. Cost, latency and results will vary based on the model used. The other LLMs, including GPT-3, provide only a one-size-fits-all model. So the costs and latency are fixed and not variable.”

When Jurassic-1 was first released, co-founder and co-Chief Executive Ori Goshen claimed that his company was the first to offer open access to LLMs without a waiting list, enabling anyone to create text-based generative AI applications. Ori Goshen added, “The Jurassic-2 family of models represents the next leap forward and will enable developers and organizations to build text-based applications, faster, with state-of-the-art performance at a fraction of the cost.”

• Data and business teams, including ESG (environmental, social, and governance) specialists, procurement teams, and deal teams, have used the SESAMm system.
• This money will allow SESAMm to grow further into the U.S. and Asian markets, assist the development of AI-powered ESG and sentiment analytics, and employ necessary talent in sustainability, technology, sales, and marketing.

SESAMm, a company located in France, uses natural language processing (NLP) technology to extract insights from its data lake, which has over 20 billion articles from free and paid sources. The company has just announced the conclusion of a €35 million (USD 37 million) series B2 fundraising round. The series B2 round was co-led by deep tech VC company Elaia and BNP Paribas’s venture capital arm, Opera Tech Ventures.

ESG and sentiment analysis of over 5 million firms

Data and business teams, including ESG (environmental, social, and governance) specialists, procurement teams, and deal teams, have used the SESAMm system. The firm offers a variety of use cases, such as ESG controversy identification, positive-impact event recognition, competitive intelligence, and quantitative investment with NLP signals created on stocks and macroeconomic variables.

Forté, CEO and co-founder of SESAMm, said, “Raising a significant amount during challenging market conditions highlights the relevance of SESAMm’s focus on two key trends: AI and sustainability. In turn, these tools enable organizations to make better decisions and fill the data gaps, particularly in ESG, in both public and private companies.”

Conventional ESG scores have been scrutinized due to their outdated nature, methodological flaws, and lack of transparency. SESAMm strives to resolve these issues by delivering current and objective information on firms. Its extensive coverage of over five million public and private businesses covers small and mid-cap corporations in Europe and emerging countries and private companies globally.

The company’s off-the-shelf ESG and reputational risk measures fill data gaps for investors and organizations. Moreover, SESAMm helps organizations discover and safeguard against emerging threats in an increasingly complex macro environment with AI-powered real-time monitoring of inflation-related online mentions in all regions.

NLP for private equity, finance, and more

The company’s strategies for developing new goods emphasize sustainability frameworks, taxonomies, and standardized reputational ratings.

Forté told one of the leading publications, “SESAMm wants to become the premier NLP platform for financial firms and corporations looking to enhance their decision-making process by leveraging insights from the web. Our ultimate goal is to be an enabler for companies looking to maximize returns and mitigate risks. We aim to make web data a real source of business insights using advanced technology. With such a keen focus on sustainability, we can only fulfill our goal of helping firms of all types to achieve their goals if SESAMm itself is truly a sustainable, profitable and responsible company.”

Asset management Unigestion, Raiffeisen Bank International’s (RBI) venture capital subsidiary Elevator Ventures, CEGEE Capital, AFG Partners, and prior investors, including Carlyle (CG) and New Alpha Asset Management, are among the other organizations participating in SESAMm investment. Each competitor participated in the last series B1 round.

This current round of investment raises the amount to €50 million (USD 53 million). This money will allow SESAMm to grow further into the U.S. and Asian markets, assist the development of AI-powered ESG and sentiment analytics, and employ necessary talent in sustainability, technology, sales, and marketing.

• With the release of the new services, customers can now access the same AI libraries that IBM uses to power its own IBM Watson products.
• According to IBM, a handful of early adopters have already tested out its new AI libraries and found several benefits.

IBM Corp. announced the launch of three new libraries within its embeddable Artificial Intelligence (AI) software portfolio with an aim to democratize some of the powerful AI capabilities it has created over the years.

With the release of the new services, customers can now access the same AI libraries that IBM uses to power its own IBM Watson products. In other words, they save developers the time and resources necessary to do it independently by allowing them to build highly advanced AI capabilities directly in their apps. With IBM’s embeddable libraries, developers now have a plug-and-play alternative to the time-consuming and complex process of training AI models that are often required.

The new libraries on offer include the IBM Watson Natural Language Processing Library, which can be used to build apps that analyze spoken human language to extract meaning and context from purpose and sentiment. Meanwhile, the IBM Watson Speech to Text Library, according to IBM, enables apps to transcribe human speech reliably and quickly. In contrast, the IBM Watson Text-to-Speech Library helps apps to translate written text into natural-sounding audio.

Each of the libraries, according to IBM, was painstakingly created by its teams within IBM Research and is intended to give programmers a simple and scalable way to incorporate AI into applications operating on any cloud platform. According to the company, they were created using a few open-source software components.

According to IBM Ecosystem General Manager Kate Woolley, “Enterprises must commit to a significant investment in expertise, resources, and time required to build, deploy, and manage AI-powered solutions. By bringing to market the same portfolio of embeddable AI technology that powers our industry-leading IBM Watson products, we are helping Ecosystem partners more efficiently deliver AI experiences that can drive business value for their clients.”

The inclusion of the three libraries will significantly expand IBM’s current portfolio of embeddable AI technologies, which already include IBM Maximo Visual Inspection, IBM Watson Assistant, IBM Watson Instana Observability, IBM Watson Discovery, and IBM Watson APIs.

According to Constellation Research Inc.’s Holger Mueller, speech recognition is a crucial feature that many next-generation applications require because it significantly improves the user experience. Mueller explained, “Speech works because humans can talk faster than they can type, so any app that has speech recognition has a leg up on its text-based cousins. So, it’s good to see more options to enable this speech-based UX coming to developers from IBM with the libraries that were previously used within Watson. More options and more competitors are a good thing for all speech-enabled apps.”

According to IBM, a handful of early adopters have already tested out its new AI libraries and found several benefits. One of these is the database business SingleStore Inc., which incorporates sentiment analysis into its offering using IBM Watson Natural Language Processing. According to Yatharth Gupta, Senior Vice President of Products at SingleStore, “Helping our clients integrate and use capabilities such as sentiment analysis will be invaluable in driving real-time analytics to help them better understand, engage, and serve their customers.”

• According to the report, almost all (95%) of the CEOs polled indicate that their organizations hastened the adoption of digital technology due to the pandemic and the requirement to adjust to remote working modes quickly.
• Around 51% of respondents stated that their firms utilize data and insights to gauge the impact of DEI, measurement is crucial.

In a recent research report titled, “Taking Diversity, Equity, and Inclusion (DEI) from ambition to reality,” Genpact gathered the opinions of 510 senior executives from significant corporations worldwide, spanning various industries. According to the report, almost all (95%) of the CEOs polled indicate that their organizations hastened the adoption of digital technology due to the pandemic and the requirement to adjust to remote working modes quickly.

In the fall of 2021, Genpact and FORTUNE Brand Studio conducted an online survey with 500 senior executives from the United States, the United Kingdom, Germany, Australia, Japan, and Canada. About 30% of respondents work at the C-level, with the remaining respondents being director-level or higher.

Insights from the Report

Of those who were asked about the effects of remote work on their organization’s operations, 50% of them said there was progress toward climate-related sustainability goals, 38% of respondents said doing so had a positive effect on their DEI objectives, 22% of them said it helped in better integration of company functions, and 5% said it encouraged employees to take part in learning opportunities.

The study found that using data and analytics in best practices helps advance and support business DEI goals. Many organizations are still having trouble taking their DEI efforts forward. The report offers a road map for companies that are lagging.

How do leading companies use data and insights to make better DEI decisions? With 51% of respondents stating that their firms utilize data and insight to gauge the impact of DEI, measurement is crucial. These insights can be used to redirect as necessary to improve DEI performance.

Following up next was the use of data and analytics to understand the strength of people’s professional networks (42%), eliminate prejudice in decision-making (40%), and increase the ability to hire and retain members of underrepresented communities (40%). According to the survey, inclusion front runners use their capabilities with data and insights to go deep into the many cultural components necessary to integrate DEI completely across all tasks, choices, and goals.

When asked about which technologies have the most potential to improve DEI, 47% selected process automation, 41% selected learning platforms, 37% voted for advanced analytics, 29% selected AI/Natural Language Processing, 28% said cloud-based technologies and 7% voted for digital communities to support affinity groups.

The survey sheds light on the need for businesses to increase their use of technology and data in today’s hybrid work module. Takeaways from this report include using data-led insights to embed DEI at all levels, encouraging employee networks, take the opportunity to meet employee needs by understanding the company culture.

• Today’s market is flooded with various companies selling vector-database technology like Pinecone. Vectara offers various services, and a vector database is just one of them.
• Cross-attentional ranking is a method used by the Vectara platform to rate results that consider both the meaning of the query and the outcomes to get even better outcomes.

Is there a more effective way to create a search tool that delivers more highly relevant results than only employing keyword-based strategies?

Amr Awadallah (CEO), Amin Ahmad (CTO), and Tallat Shafaat (Chief Architect), all former Google employees, set out to answer this question and other concerns with their new company, ZIR AI, which had been in stealth mode. ZIR AI is now coming out of stealth under the name Vectara with the aid of USD 20 million in seed funding and the availability of the company’s neural search-as-a-service technology.

Vectara’s core idea is that Artificial Intelligence (AI)-based Large Language Models (LLMs), along with Natural Language Processing (NLP), data integration pipelines, and vector techniques, can generate a neural network with numerous applications, including search.

Awadallah said, “At the heart of what we have built is a neural network that makes it very simple for any company to tap that power and do something useful with it. Large language models and neural networks have transformed how we understand the meaning behind the text, and the first offering we’re launching is neural search-as-a-service.

How Vectara is combining many AI approaches into something new

Vectors serve as a fundamental building block for LLMs and neural networks. “One of the key elements of making large language models and neural network inference is a vector-matching system in the middle,” Awadallah said.

According to Awadallah, neural networks’ input data and the output of the network are the vectors that represent the learnings produced by the network. He emphasized that the Vectara platform spans the entire data pipeline and goes beyond just examining vectors.

Today’s market is flooded with various companies selling vector-database technology like Pinecone. Vectara offers various services, and a vector database is just one of them.

According to Awadallah, when a user raises a query, Vectara utilizes its neural network to translate the request from the language space — which includes vocabulary and grammar—into the vector space, which consists of numbers and math. Vectara then indexes all the information a company wishes to search in a vector database, identifying the vector most closely related to that user query.

A substantial data pipeline that ingests diverse data types feeds the vector database. For instance, the data pipeline can comprehend the structure of PDF and ordinary Word documents and knows how to handle both formats. The Vectara platform makes use of a cross-attentional approach to provide results. It considers both the meaning of the query and the outcomes to get even better outcomes.

Going from big data on Hadoop to neural search-as-a-service

Vectara is not the only startup Awadallah has helped launch. Awadallah also cofounded the Hadoop service company Cloudera in 2008. Lessons from his experiences are being used to guide decision-making at the new firm.

One of the things he has learned over the years is that creating technology only for its own sake is never a good idea. Vectara’s neural data processing pipeline is strong and has a wide range of potential applications, according to Awadallah. At Vectara, they started with a search because it’s a problem that many firms have.

“We wanted to start with a problem that everybody has that needs to be solved in a good way,” Awadallah added.

Awadallah and other cofounders have had prior experience working at Google, a company that used transformer approaches and LLMs. He noted that a transformer makes it feasible to comprehend context more thoroughly to improve a query’s outcome. With the help of a transformer, a firm can not only understand the meaning of a word but also comprehend how the word is linked to other words in that sentence and the previous ones and then the following sentence to get the right context.

He stated, “We did this at Google. We know how to properly fine-tune the parameters to get the best outcome for our customers, and that’s truly what differentiates us.”

Vectara doesn’t just offer search as its first offering. According to Awadallah, his business will gradually add more services, with tools to assist customers in surfacing related topics and making recommendations.

“The Industrial Revolution was about how we make stuff with our hands, and now we’re helping people to build things with stuff that is coming out of their brains. That’s the foundation of this pipeline that we’re building, which is a neural network pipeline that allows you to process and extract value out of data,” Awadallah added.

• Big data backed by AI and behavioral psychology can effectively forecast how people will respond to marketing messages.
• The emergence of AI technology has minimized the need for human intervention to resolve customer service issues.

A firm’s success in today’s market is no longer dependent on product or price but more on satisfactory Customer Experience. And when it comes to enhancing Customer Experience (CX), Artificial Intelligence (AI) is a potent tool, helping companies meet modern-day consumers’ demands. Increasingly, businesses are embracing AI to enhance Customer Experience and satisfy the needs of modern consumers.

As per reports, AI has the potential to enhance company profitability by an average of 38% by 2035. AI holds the ability to enhance customer engagement, foster brand loyalty, and increase customer retention. AI can also merge other technologies, including machine learning, deep learning, and natural language understanding, to eliminate communication obstacles and automate consumer interactions.

AI is a game-changer, for sure. But is this technology effective? Would updating your Customer Experience strategy make a difference? Let’s examine some incredible ways AI is helping businesses create a meaningful client experience.

Why Should Businesses Focus on Customer Experience?

Individuals now recognize the distinction between User Experience and Customer Experience. However, the latter is fast becoming the key to unlocking unmatched market possibilities. It has now become important, helping businesses strategize marketing initiatives and better understand customers. Marketing teams are now making sense of insights to provide superior customized experiences.

Here are a few interesting statistics that every business must consider:

• According to Hubspot, customer-centric businesses are 60% more lucrative than non-customer-centric businesses.
• A report by Super Office states that 86% of shoppers are prepared to pay more for an exceptional customer experience. Furthermore, Customer Experience impacts spontaneous purchases, as 49% of shoppers have made impulsive purchases after obtaining a more customized experience.

Role of Artificial Intelligence (AI) in Customer Experience Improvement

AI helps predict customer behavior and needs: Predictive AI is a technology that helps curate experiences for individual consumers. It analyses past purchase history and actions to predict the customers’ interests and sends information when it’s time to repurchase.

Still wondering How AI improves customer experience? Here is the answer: AI provides organizations with copious amounts of real-time user data. AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) allow organizations to collect and analyze user data in real-time and adapt to changing consumer expectations and behaviors.

AI-powered insights improve decision making: Traditionally, businesses have relied on manual data collection and a few strong instincts to make business decisions. Investing in Big Data and AI is now universal, with leading Fortune 500 companies betting on it.

AI and machine learning allow insights to be powered by data. The former assists businesses in analyzing user behavior to discover patterns, detect problems, or disclose insights that enable them to enhance the interface of their website or app.

Marketing becomes more targeted and effective: AI has played a crucial role in understanding and influencing client behavior. According to 2021 research, tailoring messages to certain personality types can be more compelling and result in increased click-through and conversion rates.

AI and big data are, in fact, complementary. Big data backed by AI and behavioral psychology can effectively forecast how people will respond to marketing messages. In addition, AI can help identify and categorize clients based on their psychological and behavioral profiles.

Offer proactive and personalized customer service: AI technologies such as machine learning and predictive analytics can unearth frequent consumer difficulties and provide insight into what’s driving user problems. When used to implement AI chatbots at client touchpoints, this data enables businesses to customize real-time customer experiences while being proactive. Leveraging AI in customer experience with personalized customer service also improves the brand experience and brand image.

Streamline workflows:  The emergence of AI technology has minimized the need for human intervention to resolve customer service issues. This not only furthers the creation of a positive Customer Experience but also streamlines internal processes.

AI can also help prequalify sales leads and guide customers through support inquiries. Furthermore, AI chatbots are able to answer basic client questions and, if necessary, refer them to further online resources. As a result, the customer support staff has fewer requests to investigate, giving them sufficient time to focus on high-priority customers. AI chatbots can also facilitate direct support requests to the right department or give context to an agent before customer engagement.

Future of Artificial Intelligence (AI) in Customer Experience

The rise of AI has been exponential. Many big firms have invested in developing next-generation applications for customer experience delivery, and many more are eager to follow suit. Businesses are beginning to rely on AI for its capacity to increase productivity, decrease costs, and save time.

Here are a few ways in which AI technology can alter the corporate sector and consumer experience:

• It will automate mundane tasks and, thus, allow humans to focus on creative things.
• It will shift the focus of business-customer interactions from one-click to zero-click, resulting in a smooth and enduring experience for the target base.
• Artificial intelligence will eliminate the practice of obtaining biased data, resulting in improved information quality.

Bottom Line

Every step of the buyer’s journey can be enhanced with AI. Its capacity to gauge the target audience, their likes and dislikes, and how they shop offers countless opportunities to enhance your organization’s Customer Experience.

AI also holds the potential to make a customer feel understood and valued. This in itself can enhance brand loyalty and help with customer retention to improve cross-selling and upselling in the future. When executed appropriately, AI can make customers feel like they are on top of their choices.

• Deepfakes are a form of Artificial Intelligence (AI) powered media that can depict a person saying something even though they aren’t.
• The FBI has lately brought attention to a growing trend induced by the adoption of remote work in which bad actors employ deepfakes to masquerade as interviewees for jobs in American companies.

Suppose the ongoing fight against ransomware wasn’t challenging enough for security teams to be on their toes. In that case, another challenge of securing the infinitely expanding galaxy of cloud computing or the Internet of Things is the new horizon of obstacles called Imposters or Deepfakes.

Let’s click that refresh button to understand what Deepfakes is and why it is becoming a problem for individuals and businesses.

Deepfakes are a form of Artificial Intelligence (AI) powered media that can depict a person saying something even though they aren’t. This AI-powered media does not just stop at an individual voice but can generate authentic visuals and diverge from reality.

The FBI has lately brought attention to a growing trend induced by the adoption of remote work in which cyber criminals employ deepfakes to masquerade as interviewees for jobs in American companies. This tendency is triggered by the rise in the popularity of remote work. These bad actors hijacked the identities of United States people to obtain access to company systems. The consequences for corporate espionage and security could not be any more significant.

Now that we’ve had a run through what Deepfakes are, let us explore the various types: 

1. Textual deepfakes: It was believed that machines couldn’t be assigned creative projects like drawing or writing in the early stages of Machine Learning (ML) and the Natural Language Process (NLP). Fast forward to 2022, when the best AI-generated writing can now compose human-looking pith and clarity. Thanks to the strong language models and libraries developed over decades by the incremental labor of scholars and data science specialists.

2. Deepfake video: The primary weapon deepfake criminals use is the creation of videos and fake photographs. Considering that we live in the omnipresent social media world, photos and videos explain stories and incidents better than regular text, which makes it the most used type of Deepfake.

3. Deepfake audio: Photos, videos, and texts aren’t the only things that artificial intelligence and neural networks can do. They can even clone a human voice. All it requires is a data repository containing an audio clip of the individual whose voice must be mimicked. Deepfake can learn from the data collected and can be replicated.

4. Deepfakes on social media: You can use deepfake innovation by creating blogs or stories to build a fake profile on the internet that would be tough for a user to spot.

5. Real-time or live deepfakes: Remarkably sophisticated deepfake technology is used by businesses to manufacture identical advertisements, by governments to resemble political opponents, and by hackers to duplicate user voices to bypass voice-based verification.

Three ways to protect your business 

How can your company combat the rising use of deepfakes even if the technology powering them grows even more powerful? Here are three ways to mitigate security risks:

1. Verify authenticity: Deepfakes are made by collecting a person’s identifying information, such as their photographs and ID information, and then using an artificial intelligence engine to build a digital likeness of the stolen person. Malicious actors frequently use pre-existing video, audio, and visual content to imitate the voice and mannerisms of their victims.

This can be avoided by asking candidates to display proof of official identification, record video interviews, or ask them to visit the company at least once. You also can send a picture of the application to the authorities and ask them to confirm whether they are familiar with that individual. Engage with the reference in conversation on a business or official forum to verify their credentials.

2. Fight fire with fire: Deepfake is a system that mimics the behaviors and mannerisms of a person by utilizing Deep Learning (DL) algorithms. The product can give you chills. When only a few data points are provided, AI can create animated images and films of humans that appear to be very lifelike.

Although analog approaches can counter deepfakes, doing so takes time. Utilizing technology like synthetic data against deepfake use cases is one strategy that can be used to detect deep fakes swiftly.

3. Accelerate digital transformation and education: Deepfakes can be combated by businesses hastening their adoption of digital postures and teaching personnel the most effective procedures.

For instance, employees will benefit from deepfake awareness because it will assist them in analyzing and comprehending the material. Any information circulating in the public sphere that appears to be ludicrous or out of proportion can be criticized immediately. With the use of deepfake threats, businesses may build procedures to verify employees’ identities when working remotely and be assured that their workers will follow the guidelines.

In conclusion,

The harmful effects of a deepfake attack are currently a major concern for governments and businesses worldwide. As a result, it’s highly advised that a foolproof and robust security mechanism be put in place. Companies can effectively fight against the numerous threat actors by adhering to deepfake fraud prevention best practices and maintaining a robust digital hygiene policy. Finally, it’s crucial to inform people (or your staff) about the subtle effects of a deepfake attack and the best ways to protect against them.

Richard Socher announced a USD 50 million fund focused on AI-based firms working on verticals like NLP, computer vision, and robotics.

Socher and his co-founder have faith that they are revolutionizing the process by delivering a unique and profound experience.

In a signal that AI startups need little replenishing, Richard Socher, the CEO of You.com and former chief scientist at Salesforce, declared the launch of a USD 50 million fund for AI-focused venture named AIX ventures. In one of his email interactions, Shaun Johnson, co-founder of AIX ventures, along with his colleagues Pieter Abbeel, Anthony Goldbloom, and Chris Manning, said their vision is to make AIX ventures a household name for AI-based venture capital.

“There is an opportunity to launch a new venture firm with some of the world’s foremost thought leaders, who have made fundamental contributions to the state-of-the-art. These thought leaders are now behind AIX with the mission to fund generations of AI-focused entrepreneurs,” Johnson said.

Socher’s announcement about the funds has come four years after the ramp-up of Google Brain founder Andrew Ng’s own AI tranche – a USD 175 million fund focused on developing new companies. It has also come weeks after ex-Google CEO Eric Schmidt promised to invest USD 125 million into AI research projects via his philanthropic venture. The AI vertical has no shortage of funds. According to a report by Stanford’s Institute for Human-Centered AI (HAI),  private investment in AI more than doubled in 2020 to approximately about USD 93.5 billion.

But Socher and his cofounder have faith that they are revolutionizing the process by delivering a unique and profound experience. Goldbloom is the CEO of Kaggle, a Google-backed firm and one of the world’s most significant data science communities. Abeel, currently a robotics professor at UC Berkeley, is a former researcher at OpenAI before cofounding assignment grading platform Grade scope (acquired by Turnitin in 2018) and industrial robotics company Covariant. Chris Manning leads Stanford Artificial Intelligence Laboratory. Last but not least, Johnson recently led the product development at Natural Language Processing (NLP) startup, Lilt.

In October 2021, AIX closed the first tranche — Fund I – and included Socher’s angel portfolio. Johnson claims that AIX Fund 1 has already around 40 portfolio companies, including Hugging face, an NLP startup, Athelas, Weights and amp; Biases, and Time by Ping. Furthermore, they are already working on fund II.

Talking about the way forward to pursue startups, Johnson said that the fund will concentrate on organizations in the seed and pre-seed phases and all the verticals across the AI spectrum, such as NLP, computer vision, and robotics. Furthermore, they are also targeting AI applications in manufacturing, warehousing, Healthcare, SaaS, MLops, data, and consumer. AIX will deliver funds, technical and business guidance, assist with recruiting and strategy, and follow-on fundraising.

Experts view: 

“We believe [that], to be a top AI practitioner, you have to be practicing at the top of the field … Socher, Goldbloom, Abbeel, and Manning have individually proven they can build impressive portfolios. Joining together as a venture firm, and moving on from their angel days, takes their potential for impact to the next level. The AIX investing partners will continue their current roles and will be supported by the full-time AIX team [and] I,” Shaun said.

“[W]e see the magnitude of the impact AI will have on humanity. At the same time, the tech is just getting started. We have decades of significant progress ahead of us,” Johnson said.

A Facebook survey mentioned that more than 50% of the consumers found it favorable to buy products from a company that facilitates chat functionality. Because of their ease of use and reduced wait times, chatbots are gaining immense popularity among both brands and consumers.

Intelligent chatbots are already able to understand users’ questions from a given context and react appropriately. Bringing together an immediate response and round-the-clock connectivity makes them an enticing way for brands to connect with their customers.

Importance of bots

Chatbots can be defined as a piece of software that is capable of communicating with humans. It can be used in different applications and websites. It offers support and assistance to the user as and when required while performing a particular task.

Many organizations are developing various types of chatbots for the smooth functioning of their businesses. Chatbots help companies improve clients’ experiences and reduce customer service costs.

One can say, a chatbot’s work is similar to that of instant messaging. A chatbot can easily simulate human conversations. It helps to fill the communication gap between a human and a machine, which can be in the form of messages or voice commands. A chatbot does not require human assistance. They are programmed to answer questions based on natural language processing. It replies using pre-programmed scripts and Machine Learning (ML) algorithms.

There are two main categories of chatbots: One that works on a series of rules, Rule-based Chatbots, and the other that uses Artificial Intelligence (AI) – AI-based chatbots. Let’s talk about both in detail.

Rule-based chatbots

Chatbots that use a series of defined rules fall under this category. This type of bots use  pre-defined rules and a set of questions. Such kind of chatbots cannot generate their answers. They need an extensive bunch of answers and smartly designed rules in order to be useful and productive.

Decision-tree bots is another name for these chatbots as a decision tree is used to guide rule-based chatbots. Here, the user is given pre-defined options that lead to the desired answers.

Rule-based chatbots do not answer those questions that are out of the defined rules. This, however, is not a drawback, as the fundamental role of such chatbots is to answer queries from the given defined set.

An example of rule-based chatbots is when they are used as an FAQ resource, and they don’t need to have multiple example conversations to feed it for the response.

Pros –

• Rule-based chatbots are highly secured and accountable.
• They are quick to learn and cost-effective.
• Faster to implement in bots.
• Easily integrable with legacy systems.

Cons –

• More of the robotic chatbot rather than conversational.
• These chatbots need manual feeding as they require training to improve manually.
• Independent operation of these bots is not possible.

AI-based chatbots

AI-based chatbots work on complex ML models. The ML model enables them to self-learn with the help of provided data and then generate the required answers accordingly.

As AI-based chatbots are trained using ML models, it helps build a better connection between the questions asked by the users in different ways and languages. These chatbots are capable of understanding the context and intention of the question before they answer them. As soon as the question’s intent is clear, they start building up their answers for more complicated questions using Natural Language Processing (NLP).

NLP supplements the chatbot’s ability to understand and respond to all human queries. With the help of ML techniques and AI capabilities, bots tend to turn smarter with time.

AI-based chatbots help divert precious human resources to some more focused tasks. Human intervention is required only when the user asks a very complicated question.

Alexa, Siri, and Google assistant are some best examples of AI-based chatbots.

Pros –

• Learns from the given info.
• Increases customer engagement.
• Understands and interacts in different languages.
• Ability to understand the context of the question.

Cons –

• Requires more data for training; cannot be trained under limited data.
• The implementation process is long and complex as it takes a larger amount of data for training.
• Correcting wrong interpretations takes an ample amount of time.

Choosing the right one!

Both rule-based and AI-based chatbots have their advantages and limitations. Here, choosing a hybrid chatbot can be the best solution. Hybrid-based chatbots work on some rule-based tasks and can understand human intent and context.

Medical Diagnosis chatbot is one of the best examples of a hybrid chatbot. Chatbots enquires about patients’ symptoms in a rule-based pattern, while AI-based chatbot is used to solve queries.

Conclusion

Companies today are showing their online presence using a website or social media channels. Such firms need to capitalize their presence using custom chatbots to communicate with their target audience easily.

Advancements in NLP have made it possible for chatbots to communicate with consumers as humans do. Implementing chatbots helps businesses save resources, time, and cost and does maximum work in less time.

In order to stay ahead in the future and achieve future advancements, organizations need to implement hybrid chatbots.

To learn more about similar technologies, visit our whitepapers here.