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The Singapore government launched its Initiative in November 2014. Since then, Singapore has supported the extensive use of technology to enrich the lives of its citizens. Artificial Intelligence (AI) solutions have been successfully harnessed to improve the efficiency and quality of services. Smart Nation Automated Speech Recognition (ASR), which is the technical name for text-to-speech conversion, has become one of the most widely used applications of AI, so much that a special flavor of ASR was created just for Singaporeans. In this article, we will explore the multiple ways in which Automated Speech Recognition (ASR) has provided value to different organizations, specifically Singapore government services. We will also look at how Voice AI and text-to-speech services were instrumental in the implementation of Automated Speech Recognition (ASR) technology for government services. Sentient.io’s The Hidden Worth of Automated Speech Recognition While most people recognize Automated Speech Recognition as the technology that makes funny conversations with Siri, Google, and others possible, it is so much more than that. In the services industry, Voice AI-based bots are a boon because they always manage to sound pleasant and patient. Moreover, they not only understand spoken words but also the context in which they are spoken. In addition to interacting with customers/users, ASR can also be applied in the workplace. ASR-enabled applications can listen to conversations or speech and generate transcripts that can be used for in-depth analysis. Transcribed or text-based records are not just easy to maintain, they also provide easy access to information via digital search. Some distinct advantages of using automated speech-to-text services are as follows. Transcribing speech to text manually is a time-consuming and tedious activity requiring hours of human effort. Automation ensures that this time can be put to better use. Automation reduces the chances of errors or omissions during the transcription process When it comes to sensitive information, it may not be possible to outsource the transcription task to external contractors due to security or privacy concerns. A well-tested ASR service that is also sensitive to local nuances in speech can help to generate transcripts with high accuracy. There are many processes in both the public and the private sector that can greatly benefit from the power of ASR. The following are some examples of where Speech-To-Text services can help in better governance. Governing bodies need to conduct meetings daily. Accurate and easy to access verbatim records of such meetings would ensure that significant information is always available. This is especially beneficial for high-level meetings or discussions that can affect laws, regulations, and policies. When it comes to court rulings, lawyers often rely on the records of past judgments to build their case. Such records can be easily generated using ASR. Parliament proceedings, speeches, and debates that are fully recorded can be transcribed for public consumption. In situations where recordings are available on video, the same service could be used to include subtitles in the video. “This conversation may be recorded for quality assurance” - That's right! Call centers usually record conversations. Analysis of transcripts of such conversations can help to address citizen pain points when interacting with government bodies. Interviews for new employee positions may be recorded and transcribed for reference in the future especially for qualified candidates who were not recruited. To enable the easy use of ASR in any locale it should be tailored to the specific nuances of the language used in that locale. Let us now see how this factor is relevant to ASR technology used in Singapore. Why and How did Singapore create its own flavor of ASR? English spoken in Singapore can be standard Singapore-English or the colloquial form, also known as . Any speech-to-text engine built for Singapore should consider nuances that are unique to Singaporean English. Following are some of the words and terms we use in our day-to-day life which are not pure English and would not be recognized by any other ASR. Singlish Words like “ang-moh” and “kiasu” are commonly seen in news articles and tv shows. Phrases like “double confirm” and “act blur” may sound English but are only unique to Singapore. Terms like “4D” (the lottery system) and “O$P$” would only be relevant to Singaporeans. Slang words like “Chope” which is also the name of a local website have become a part of a normal Singaporean conversation. Existing speech technologies were not capable of transcribing locally accented English accurately. To achieve high accuracy, Sentient.io trained its model on . This corpus contains 1 Terabyte of annotated speech audio. This ensures accuracy in transcription when spoken English is accented or colloquial. Let us now see how this technology helped some of the government services to build ASR based solutions. Automatic Speech Recognition IMDA’s National Speech Corpus (NSC) Singapore Government Services That Benefitted From ASR Sentient.io’s expertise in ASR technology has enabled several Singapore government agencies to build robust solutions using ASR. Following are some agencies that have benefitted from Sentient.io’s ASR solutions. Singapore’s largest content creator and national media network for both radio and television which produce all kinds of content for TV and radio, including dramas, news, talk shows, reality tv, documentaries, etc. Sentient.io’s ASR has helped them to improve their productivity in News Reporting by 500% and Drama Subtitling by 600%. The Ministry that oversees information and communications technology, the media and design sectors, public libraries, as well as the Government's information and public communication policies engage Sentient.io’s ASR to help with the transcription of the . This included a transcription of speeches by ministers and senior parliamentary secretaries who participated in the event. Committee of Supply 2020 How can you benefit from Sentient.io ASR? Sentient.io not only possesses the expertise in developing ASR solutions for government services, but the Sentient.io in-house team can also quickly customize their ASR services for a . broad range of industries and use-cases Also published at https://medium.com/sentient-io/automated-speech-recognition-helps-singapore-government-services-to-increase-productivity-83b39d59d0e3

Automated Speech Recognition Increases Productivity In Singapore's Government Services

AI is not just a techy buzzword anymore. We regularly come across AI/ML technology in both personal and business use applications. Businesses are looking at AI for ways to increase revenue, improve operations, and to enhance productivity. If you are looking for readily available AI solutions that could accelerate your business, then dive straight in. We will be exploring some unique but probably underrated scenarios for introducing AI solutions in businesses. You will also discover how can help you leverage AI to achieve business goals. Let us start by understanding which areas of business can benefit from AI. Sentient.io What is The Democratization of AI? According to a recent , 77% of the organizations surveyed planned to either increase or retain their AI investments in spite of the global pandemic. Additionally, the report also talks about the “Democratization of AI”. This implies that businesses want everyone linked to the organization to experience the benefits of AI solutions. This includes customers, business partners, business executives, salespeople, assembly line workers, application developers, and IT operations professionals. How exactly can this sort of democratization be achieved when every aspect of the business functions differently? Gartner report In spite of the differences, there are many common practices across departments that are tedious and still carried out manually. For example, why do we still need someone to jot down meeting notes? While most businesses appreciate the value that AI solutions can provide, they are unaware of the common areas where automation can increase productivity. Fear of the unknown acts as a barrier to getting started. Also, acquiring the required expertise or knowledge to build and subsequently work with AI solutions is another challenge. Sentient.io can help businesses by providing a trusted source of expertise to get started on their AI journey. Let us find out the relation between the different business goals and available AI solutions. How To Transform Your Business With AI There are several ways in which AI can be integrated into your day-to-day operations. Let us look at how different AI solutions can be used to achieve specific business goals. : Your sales revenue largely depends on better customer understanding and improved customer service. AI solutions that analyze data from both new sources like CCTV and beacons, in combination with existing sources like POS can provide a deeper understanding of customers/users. This can help to influence customer spending habits and increase stickiness. AI solutions that suggest optimal product placements in retail stores or online product recommendations are results of such analysis. Increase revenue : To improve operations, you need to identify redundant or lengthy processes that can be optimized. Again, operational data analyses can be used to identify and gain insights into operational inefficiencies. These can be subsequently addressed using problem-specific AI solutions that eventually result in a reduction in unnecessary operational costs. Improve operations : AI solutions provide intelligent tools that can reduce effort, time, and financial investments by automating manual or expertise-heavy processes. They can be trained to deliver an equal or similar quality of output at a fraction of the initial resource investments. Enhance productivity Let us now look at a few common business scenarios that can be optimized with the use of AI. AI Applications That Address Your Mundane Problems We will now look in detail at five challenges that are common to many businesses and AI solutions that can be used to address these challenges. 1. Meeting notes The process of taking down meeting notes usually does not cover all spoken content and is prone to human errors. Without recorded texts of the meeting, it is impossible to run a textual search to find or reference discussions that happened in the meeting. The result is lost productivity (in recalling or listening through the recorded audio, if any) and errors (in an incorrect recall or misinterpretation due to imperfect memory). Voice AI solutions like from Sentient.io can process such recordings and transcribe them to textual meeting notes with high accuracy. This text may be further processed to add context, before sharing it with all participants and stakeholders. automatic speech recognition 2. Customer profiling In the retail industry, customer profiling helps businesses to understand the spending habits of customers and design targeted marketing campaigns aimed at a particular type of customer. Customer profiling is usually unidimensional, from a single data source that belies the multifaceted nature of customer behaviour. Such data may not be enough to provide a good understanding of customers and subsequently result in poorly informed business decisions. Data about customers may be available from other sources, which can help to build an accurate picture of the customer. For example, a retail chain may combine CCTV data with POS data to better profile their target customers at different locations. This would help to increase revenue while allowing for better management of inventories.  A Sentient.io in specific departments of the store can help to identify items that were seen but not purchased by customers. solution that can count the density of people 3. Podcast production The podcast production process requires an effective voice and clear-speaking capabilities, in addition to a lengthy process of metadata creation. You may also want the podcasts to be delivered in specific accents depending on the audience demographics. The cost of production is directly proportional to the human effort involved in most cases. However, AI-based services from Sentient.io can help you to not only edit but also create podcasts from scratch. Such services are available as affordable SaaS solutions. text to speech 4. Extraction of financial information Businesses may be required to extract financial information about individuals in situations where background checks are required. Extraction of such information manually is a laborious process because the information available is unstructured and distributed across different types of sources. The Sentient.io individual search combines data from different sources and provides it in a readable and manipulatable format thereby addressing the operational inefficiencies of the extraction process. solution 5. Video Editing Creating short-form videos from long-form videos is a laborious and time-consuming process that requires adequate skills and experience. There is a high cost attached if you engage internal or third-party resources to perform this task. An AI-based video processing software could provide the solution to this problem by analyzing the video content to identify key scenes in the video based on certain criteria and using these to build a shorter video.  This solution would eliminate the requirement for human effort and provide businesses with an affordable SaaS solution. How can Sentient.io help Sentient.io provides many diverse and valuable tools and solutions through their AI and Data microservices platform that can address varying business needs across industries. Sentient.io microservices can be easily integrated into your solutions and provide pre-built and pre-tested AI functionality in the following areas. 1. Computer Vision Object Detection People Counting People Recognition 2. Natural Language Processing Word-sense Disambiguation Entity Recognition (Pick out key entities from a sentence) 3. Voice AI Speech to Text (Automatic Speech Recognition) Text to Speech Additionally, it provides data services by collating information from trusted sources. These services can be used to perform background checks on individuals as well as businesses from the convenience of your office. Similar to AI microservices, these analytics services also provide easy integration features that can facilitate processes such as one-click loan review in banks and financial institutions. Thus, Sentient.io can help you address multiple business challenges through AI solutions that can be integrated into your business processes. Here we have presented a few scenarios, but there are many more. To learn about the many other ways in which your enterprise can benefit from AI solutions, download our full report available at https://www.sentient.io/ai-colab-lp?utm_campaign=Signups_Content_01_2021_Five_remarkable_AI_solutions_that_help_to_achieve_business_goals&utm_source=Hackernoon Previously published at https://brandzasia.com/five-remarkable-ai-solutions-that-help-to-achieve-business-goals/

Reach Your 2021 Business Goals with AI Solutions

When you use an AI-enabled application like a chatbot, a voice assistant, or the smart reply feature in Gmail, do you wish you could build a similar smart application too? If you want to know how these applications got their cognitive abilities and in-depth knowledge and learn to build one yourself, you have reached the right page. In this article, we cover a few basic AI concepts and scenarios that can be used to enhance almost any daily-use application. We also look at options available to build these capabilities. Finally, we introduce to you a shortcut with which you can build your own mini-AI application within just 15 minutes of reading this article. With some API’s, you can achieve this and much more. So let us get started on our AI development journey with some ABC’s. The ABC’s of AI-enabled applications Robots, chatbots, and voice assistants use one or many AI/ML concepts in the background. Let us have a quick look at some fundamental concepts and their applications before we proceed. AI models are algorithms that are “trained” using large datasets to learn and understand patterns. This enables the AI model to intelligently predict an output given some input information, classify items, and much more. An AI-enabled solution typically uses one or more AI models. All of the following concepts are implemented using different types of AI models. AI Models - The building blocks NLP allows machines to understand and process input or data that is provided in human languages like English. Chatbots use NLP to understand and process questions from users. NLP is also used in voice assistants, for sentiment analysis of posts on social media, and by email providers to understand incoming emails before suggesting a reply. Natural Language Processing  (NLP) As the name suggests, computer vision allows machines to process image data to understand what the image represents. Computer vision can be used for biometric authentication, quality control in factories, automated checkout in the retail industry, or for medical diagnosis of scans and x-rays. Computer Vision While NLP helps to process the language semantics, Voice AI deals with natural speech with all possible variations in voice, pitch, fluency, accents and intonations. It is used in voice assistants and many other smart devices and apps that can understand what we are saying. Voice AI With this basic understanding, we can now proceed to look at the tools, components and steps required to develop an AI-based application. Developing an AI-enabled application from scratch Several businesses today try to integrate AI features in their internal as well as consumer-facing applications. Once the requirement for an AI component is established, the development takes place in several steps. : The AI development team needs to determine the inputs and the expected output corresponding to possible combinations of inputs. Data collected thus is cleaned before it can be used to train the AI model. Data collection : Prediction or classification machine learning algorithms are created that can learn from the dataset. Machine learning libraries such as TensorFlow or Pytorch may be used for this purpose. Create an algorithm : The algorithm is then trained using the data collected. During this process, the algorithm may need to be optimized in order to achieve an AI model with high accuracy. Additional data may be required in certain situations. Training : Finally, an AI model with high accuracy can be integrated into the overall solution. Integration One can directly get to the integration stage by using pre-trained AI models that are available as APIs. These provide solutions for commonly required AI features and may be integrated directly into the overall solution. APIs can be accessed via simple calls with the input and return the desired output with a high degree of accuracy. Let us see how exactly API’s prove beneficial to the development process and what are the different types of API’s available AI APIs to shortcut your AI solution building process APIs help you to cut short your development time and effort. Instead of developing something from scratch, you use features and functions that have been developed and fine-tuned over the years by experts in the field. When it comes to AI, APIs provide a readymade solution to the tedious problem of collecting and cleaning large amounts of data before you even begin training your model. Different types of APIs available today are. : Also known as a Public API, there are no restrictions to access an Open API as they are publicly available. : A developer needs specific rights or licenses in order to access this type of API because they are not freely available to the public. : Also known as Private APIs, only internal systems expose this type of API. These are usually designed for internal use within an organization. By using internal APIs to communicate between systems, an organization can improve the efficiency of its processes and systems. : This type of API combines different data and service APIs. It combines multiple transactions sequentially so that the client can get the desired result in one API call instead of many calls to the server. With this introduction to APIs, we can now explore Sentient.io API’s in further detail. Open APIs Partner APIs Internal APIs Composite APIs Sentient.io APIs to implement expert AI solutions When it comes to popular AI APIs, you will often find APIs in the fields of Computer Vision, Natural Language Processing, and Voice AI as the use cases are widespread and the research in these AI fields is relatively mature. Sentient.io APIs cover use cases in all of these areas. For example, Object Detection People Counting People Recognition Computer Vision Word-sense Disambiguation Entity Recognition (Pick out key entities from a sentence) Natural Language Processing Speech to Text (Automatic Speech Recognition) Text to Speech x1 Voice AI With this, we come to the most interesting part. Let us start calling these API’s from your code and build an AI application. A Quickstart for using Sentient.io AI APIs Users are required to on Sentient.io in order to start using the APIs. The registration is free of cost. After registration, you can explore the apps that are available on the platform. This should give you a fair idea about the capabilities provided by different Sentient.io APIs. For example, the following image shows a demo app that is able to provide identify objects in an image. Step 1: Register for free register Step 2: Discover and select the right API for your use case demo Once you have identified an API ( ) that you would like to use in your application, you can subscribe to it and use it. A limited number of API calls are free so that you can try the service before purchasing it. Microservice A typical API call consists of the following components: : This endpoint is provided by the API provider, your HTTP request and payload will be sent to this endpoint. : The API key is a string that is unique to your account to allow the API provider to authorise your request. You can find your Sentient.io API key on the microservice subscription page. : Here you will include the input information in the format stated in the API Documentation. The API documentation can be accessed from the subscription page on Sentient.io. With these components ready, you can now construct your request in your development language. With Sentient.io, you can also select from popular languages on the platform and copy the pre-constructed code snippet to your application as shown. Step 3: Construct your API Call Endpoint URL API Key Payload Your application is now AI-enabled. You can initiate a request to the API through the application to ensure that the connection is in place and you are getting the expected response. Thus, in three quick steps, you have a ready-to-use AI-enabled application. You may have come across other API providers. The next section tells you how Sentient.io stands out from other similar service providers. Why should you choose Sentient.io? AI-enabled solutions not only help to save time and effort but also complete tasks at a scale that would otherwise be considered impossible. In many cases, they are able to outperform humans in making accurate predictions. Using AI in your applications will help your app to stand out from the competition, as well as deliver greater value to your users. With Sentient.io’s APIs, you can add AI capability to your applications and solutions in as little as 10 minutes. Sentient.io’s services are domain targeted. This implies that different flavours of the API have been trained using data specific to different domains. For example, if you train an Automated Speech Recognition AI Model on an American accent, it will be optimized for the American accents. Sentient.io’s microservices are optimized for specific domains so that you can get better accuracy when you select the right AI model for your use case. Sentient.io is different from API Platforms who compile APIs from different 3rd parties and are not responsible for their performance. Sentient.io understands that reliability and security is a top determining factor in incorporating APIs into your application. Hence Sentient.io has a variety of Service Level of Agreements (SLA). What this means is that performance will be guaranteed according to the SLA applicable for that microservice, giving you confidence that you can rely on the service to be up when you need it. Sentient.io is able to deploy microservices on-premise or in an air gap environment. This means that we can deploy our platform completely detached from the internet; this is useful for companies who have very strict security requirements such as government agencies and banks. Stand out from the competition AI-enable your app in 10 minutes Reliability and Security Sounds promising? Join us now! So if you are eager to kickstart your AI development journey, now is a good time. Sign up on  Sentient.io as a free user and you receive complimentary 2000 Sentient.io credits that are worth USD $20. These can be utilized to buy additional API usage on Sentient.io. Use the promo code GetStarted2000 during sign-up.

How To Build Your First AI-Enabled App

As a known fact, the retail scene globally has been in a slump ever since the rise of e-commerce and digital devices. The pandemic-stricken year has pushed the sector further into the gloom, with experts predicting sales to be declining by at least 10% year-over-year in the Southeast Asia region. On top of that, businesses are grappling with the challenge of engaging increasingly distracted customers in the digital space. With the tighter squeeze, brands are also re-looking at innovative yet cost-effective ways to enhance customer service experience in order to build loyalty. Recognising these challenges that companies are facing, has launched an online hackathon, the iHack Alpha, to tackle the aforementioned problems through AI-enabled solutions. The hackathon, which concluded on January 5th, saw over . Developers could work either individually or form a team (with a maximum of four members) to work on any of the four stipulated themes: Retail, Customer Service, Media, and Accessibility. Sentient.io 1,000 participating developers In each of these themes, a problem statement is provided, and developers are free to identify opportunity areas they would like to explore and ideate upon. To illustrate, the problem statement given under the theme of retail is: How can you use AI to help retail evolve and stay relevant? With this, developers could, for instance, identify how more retailers are adopting the online-to-offline (O2O) strategy to drive foot traffic to physical stores. Developers can then propose and design tools that will help businesses overcome the digital blind spot in marketing when going from O2O. Contrary to the common “job-stealing” notion that has been assigned to AI advances, the many innovations that stem from this hackathon alone, have proven that . AI can serve as an enabler Among the eight shortlisted ideas to the prototype phase, is the GeoFencing project. The project aims to help market the small local businesses or mom-and-pop stores that are overshadowed by giant corporations and do not have an online presence. The solution is to develop extensions or mobile apps that will help consumers to locate products that they are keen on purchasing, nearby. The idea is to help people get to know the shops in their vicinity. This provides the smaller businesses the opportunity to be considered as an alternative, without having to spend exorbitant dollars on advertising. To set the idea in motion, the team had employed various microservices from Sentient.io, including the Object Detection Microservice, Web Scraping API, as well as the Base 64 Encoding and Decoding Service. Empowering the Disabled at the Heart of iHackAlpha With a large proportion of the submissions targeting the accessibility theme, the single socially related theme is seen to have gained the most traction among contestants. An 80% unemployment rate in Southeast Asia for persons of disabilities is indeed a note of concern. Many teams of developers have expressed with full conviction in their presentation that this societal group has just as much to contribute as the general population if they were provided with proper assistive technologies. “Even with valuable skills and experiences, visually impaired people often find great difficulty in securing employment. After so many technological advancements in the last few decades, we still fail miserably at disability inclusion in the workplace. One of the main reasons for this lies in the fact that organizations fail to deploy assistive technologies for the visually impaired at the workplace. The most sustainable way to include the visually impaired population in the workforce is by considering it as a value-add transaction instead of going from a social responsibility perspective. Considering disability inclusion as a value-add transaction means that we want to bring in a disabled person into the workforce not because it is a social responsibility but because they have the necessary skills and resources to bring value to the organization. Therefore, in order to solve this problem of blind inclusion in the workplace, we must focus on making assistive technologies more accessible to this population,” said one of the finalists. The group then went on to propose a smart glass, which they named , to help visually impaired persons perform more productively at the workplace. iGlass “In this device, we attempt to integrate features from different assistive technologies like screen readers, canes, etc into a single device using recent technological advancements in artificial intelligence and embedded systems. The visual data from the surrounding is captured using a camera mounted on the smart glass. This visual data is then fed into a pipeline running on Raspberry Pi to generate auditory data using different sentient.io microservices and AI models,” the group explained. In other words, the necessary information captured through features such as image captioning, face recognition, emotion recognition, optical character recognition is then conveyed to the person seamlessly in audio format, through the bone transducer. It simulates the voices in one’s head that relay information regarding the immediate surrounding. “I was impressed with iGlass, though Google Glass had attempted this and failed, at least for now.  It was amazing how much the team accomplished in a short period of time,” said one of the six judges, . Ms. Christy Davies, Executive Director of Lien Centre at Singapore Management University When asked what she was looking out for in the submissions, Ms. Davies said: “First and foremost, did the invention or solution meet the needs of their defined client?  I looked for a clear problem statement and a systematic thought process which then used data or AI as the tool and vehicle to tackle their problem. I was also curious to a clear motivation of why they chose the issue they did because a solution is often more effective when it is emotionally or very practically connected to the heart and passion of the entrepreneur or coder”. It was certainly heartening that despite it being a hackathon contest, many teams bore the common goal of empowering and improving lives through their innovative ideas and skillful application of AI technologies. Winning Concepts and Prototypes The project which clinched was titled the , a two-way chatting app for blind-deaf people to communicate with others. To send a message, the blind-deaf person can simply tap the screen, using either short tap (representing dots) or long taps (representing dashes). The Morse code written by them is then converted into English text for the other party to read. On the flipside, incoming messages from the other party are converted into vibrations of dot & dash for blind-deaf people to understand. first place Artificial Voice “The idea for the project is inspired by the Helen Keller story we studied in school days, where Anne Sullivan used to write on Helen Keller’s palm to communicate with her. So, we thought why can't we replace Annie with a mobile phone, which can communicate with the deaf-blind by sending a message with haptic feedback,” said Mr. Harsha Satya Vardhan, the team leader of project . Artificial Voice Taking into consideration that subjects could be together at the same place or communicating across different locations, the team behind the project has also included the ‘near’ and ‘far’ features. The ‘near’ feature can be activated when both parties are at the same place. When the person without a disability speaks, it is converted into vibrations via Morse code in real-time for the blind-deaf person to understand the message. The blind-deaf person will also be able to give a reply in real-time by tapping on the screen of the app. Using Sentient.io Text to Speech and Automatic Speech Recognition services, the Morse Code is converted to speech that is heard audibly by the other party. “I like the Artificial Voice submission because it is something which can really benefit a group of very marginalized people and bridge some of the communication gaps with others.” said one of the internal judges, Eddie Leong, VP Operations of Sentient.io The team behind was overjoyed when they were announced as the winner of iHack Alpha Challenge. Artificial Voice “Mahesh and I have been working very hard for three months on this project and had many iterations. We look forward to improving the current prototype and the lives of the visually impaired individuals through our project,” said Mr. Vardhan. The were awarded to project and respectively. The former is a website that allows people with hearing impairment to take voice notes during a meeting. Meanwhile, the latter is a mobile app that resembles a virtual mentor or friend for people with neurodevelopmental disorders, including Autism spectrum disorder and dyslexia. It works as a translator that translates pictorial representations to normal text and vice-versa to fill the communication gap in a virtual working environment. second and third places MeetDocs Buddy With regards to the concept behind Buddy, the team explained that people with neurodevelopmental disorder often face difficulty comprehending long paragraphs, along with the tone. The app can help users to identify a variety of tones present in a sentence, including the emotional aspects and language style. It also provides a visualisation of words, as well as some related content (Wikipedia article) for better understanding. The project is intended to provide these employees with greater ease of communication and in turn, efficiency at work. “The biggest challenge we face while working on our project is managing our time since we have limited time developing, planning, and testing the products. The other challenges we face were creating an impactful solution that addresses the problem and attempting to solve it to a more considerable extent. “But our desire to make people's lives better using technologies and the support of Sentient.io mentors helped in the successful completion of the product,” said Mr. Ashutosh Gupta, the team leader of project Buddy. Among the judges was , who expressed that he was looking out for solution level, business scalability, and profitability. He noted that the submissions contained unique ideas and concepts, which completely matches the frame of open innovation and intent of a hackathon as such. Mr. Etsushi Tsuru, Managing Director of Asahi Shimbun Asia Other criteria include, but are not limited to code quality, the technology used, the overall usability, and the user experience. About Sentient.io empowers developers and enterprises by enabling easy integration of AI capabilities into their solutions and software through the Sentient.io AI & Data platform. Sentient.io Our platform allows developers with no AI experience to easily find and use powerful pre-trained AI models as well as data sets through API calls, allowing you to rapidly and effortlessly add AI capabilities to projects. Also published at https://mark-at-sentientio.medium.com/ihack-alpha-by-sentient-io-unveils-ai-powered-solutions-for-business-social-challenges-2209d0ae3716

iHack Alpha Inspires AI-Powered Solutions to Address Social Challenges

As technology penetrates every facet of life, and continues to grow exponentially, the solution potential becomes enormous. At the same time, we're in a world where billions live in poverty, and millions are on the brink of famine. In order to support an ever-growing populace, we need to leave no stone un-turned in the search for solutions. AI provides many potential solutions to humanity's greatest challenges. "AI" is a vague, even confusing term. If you hear the phrase "artificial intelligence," you might wonder why there aren't sentient robots walking around, or why everyone isn't in self-driving cars already. The reality is that "AI" is just a marketing term for a set of computational statistical tools, or more simply, algorithms. However, as versatile as mathematics is, so is AI. AI is limited by (primarily) a couple things: data and computational power. Both the data and the compute power we have available are growing exponentially, so AI is becoming more and more powerful. With this increase in data and computational ability, AI is now being used in a wide variety of applications. For example, (disclaimer: I'm CEO), collects meaningful AI problem statements to crowd-source solutions to data scientists. Some problem statements include saving animals’ lives, increasing agricultural yield, and speeding up healthcare claims processing. Michael Suttles, CEO at Save All The Pets, explains how data and AI can be used to save shelter animals: bitgrit “I'm planning to use data science to save animals' lives. Long story short: I’m going to collect data about which dogs are more likely to be adopted in which locations, and then relocate dogs to where they’re more likely to be adopted. For example, if Terriers are more likely to be adopted in Dallas than Houston--or older dogs are more likely to be adopted in Chicago than Raleigh--we’ll move them there.” More specifically, AI models such as NLP can be used to analyze the text description of an animal in a shelter, and Convolutional Neural Nets can analyze images of the animals and help determine the probabilities of animals being killed or adopted in different shelters. Dog breeds are a genetic mechanism - not visual, so you can't for certain what a dog's breed is just by looking at it. Therefore, creating a Convolutional Neural Network to predict a dog's breed is harder than it sounds. know And CNN's are data hungry, as are . Given that there are literally , and we need data for each breed, we'll need tens of thousands of labelled images. Fortunately, bitgrit was given such a data-set. roughly 80,000 images of dogs, with their breeds attached as labels. While a CNN would not strictly be necessary for classifying dog breeds, we'd recommend it to . most neural networks hundreds of dog breeds We gave users get started very quickly What about increasing agricultural yield? As with the dog breed challenge, the agricultural challenge is highly complex, so it's not as if an accurate model means that we have an end-to-end solution for the real-world, but it is a step in the right direction. Suvrajit Saha, Founder at Klimazone, explains how AI can be used in agriculture: “Agriculture is shifting to precision farming methodologies, allowing you to use less water and chemicals and reducing its environmental impact… The first step in gathering information about a specific crop is to identify the field from satellite imagery.” In this problem, we attempt to use AI to determine contours of agricultural fields, kicking off the pre-requisite steps for precision farming. To add some difficulty into the challenge (as contour detection could be done without AI, for instance with canny edge detection filters), we include crop detection in the task. Although seemingly completely different, crop detection and dog breed detection are surprisingly similar. Just as users were given dog images with breed labels for the dog challenge, users will be given field images with crop labels for the farming challenge. As before, one potential method would be using a CNN, but this challenge is a lot trickier, as you'll be looking at aerial satellite views (and a lot fewer of them), instead of portrait pictures. Don't buy into the hype that says AI will have real intelligence tomorrow, or the naysayers who say AI is just a fad. As always, the truth lies somewhere in the middle - AI is just a tool, and it's up to us to use it properly!

We're collecting AI problem statements to crowdsource solutions to data scientists

It sounds like science fiction, but with all the hype around AI, the danger of ‘kidnapping’ is worth talking about. It goes like this - companies who can afford to build their own internal LLMs throw all of their valuable IP (intellectual property, covering everything from trade secret designs to marketing campaigns and product strategies) into the model for it to generate relevant responses. Essentially, while it’s still a non-sentient AI model, it’s also a repository of all the most valuable information the company has. This makes it a fantastic target for a criminal attacker, or even an unethical competitor. If it can be poisoned to generate bad responses, cloned to give the attacker more insider knowledge than they know what to do with, or even somehow locked down for ransom, then it can do a huge amount of damage to the company. Companies struggle to secure their systems, with regular headlines showing large enterprises being breached. An LLM model, containing everything about the company, is going to quickly become a favoured target for virtual ‘kidnapping.’ Whether it’s a case of adapting ransomware to encrypt models, exfiltrating them and destroying the local copy, or subverting them into an insider threat (say, if the LLM has been integrated with the company’s invoicing and finance systems without effective human oversight), the potential for harm is dramatic. Making it worse, the value of these models will come from their availability and accessibility within the company. If no one can use them, they’re nothing but a resource sink. They must be used effectively to have value, and that means they must be accessible. How do we match the need for availability with the need for security on these systems? Is Software Security Enough? Maybe software-defined networks could be a solution? The problem is that any software solution is inherently vulnerable to compromise. And if we’re talking about an LLM which has been integrated with administrative systems (this is already being done, the ethics and risks of this are a whole article in themselves - maybe a book), then it would be simplicity itself for a malicious party to prompt the AI to create a way in for further attacks. Software solutions, and classic devices such as firewalls and data diodes, definitely have a place in a secure AI system design, but something with so much value to a company justifies a much more absolute approach. If we go old-school, what we’d talk about as the last resort for any attack is pulling the plug. Ultimately, unless an attacker can change the laws of physics (or is sitting inside the data centre, which is unlikely) unplugging the network is pretty unbeatable. The problem is that unplugging and reconnecting the network takes time - making a phone call to your network engineer to plug it in or out every time you want to send an authorised prompt doesn’t seem a great option. The Goldilock FireBreak I recently came across a security device which impressed me, which doesn’t happen often these days. It wasn’t because of its brand new quantum dark web AI firewall technology, as so many vendors advertise these days. Instead it was because it’s a company which has taken a very old idea, thought about it, and brought it bang up to date in a way that opens up a lot of possibilities for secure network topologies. The approach is simple, and pretty unbeatable (obviously nothing is 100% secure, but the attack vectors against a physical disconnect are vanishingly sparse). Essentially the FireBreak (as opposed to firewall, of course) unplugs the network cable on command - or plugs it back in. It’s not quite having someone on-call in the data centre to manipulate cables on demand, but that’s an easy way to think about it - except the person can plug or unplug the cables in a fraction of a second in response to prompts from any method you choose. Often management networks are separated only by a firewall, whereas FireBreak itself has a control plane completely out-of-band from the network. I can easily imagine tying together FireBreak with your ticketing system to only physically connect the management planes during defined change windows, making them unavailable even to the most privileged attacker without going through a documented process. “Operating in a so-called ‘post-breach era’, the security industry seems resigned to the idea that cyber-attacks are a matter of ‘when’ and not ‘if’. At Goldilock, we wanted to find a way to empower individuals and organisations to take more accountability and control over what happens to their data. Our flagship product, FireBreak, was born out of this idea. Its hardware-based approach enables users to physically segment all digital assets, from LLMs to entire networks, remotely, instantly and without using the internet. This allows users to physically connect and disconnect when required, from wherever they are in the world, hiding any assets from view and enhancing their existing depth of defence, including against AI, which is essentially just a type of software. Ultimately, we want people to rethink what needs to be kept online and see a move away from the "always-on" model that can leave sensitive data exposed. Because any data kept online is at risk of being breached. With more and more companies training their own LLM models in-house, a new realm of security considerations needs to be made. Network segmentation should be part of a holistic cybersecurity strategy that keeps bad actors out and ensures they can be stopped in their tracks if they do breach external parameters." -- Tony Hasek, CEO and Co-Founder of Goldilock So how does this help with the LLM kidnappings? LLM models are big. No one is going to clone one in seconds. They can also be used asynchronously - they don’t depend on a constant connection to generate a response to a prompt and could easily reconnect and send it when finished instead of working in an interactive session. There is no need for a user to have an active session to the LLM while they’re generating a prompt. There’s no need for them to have an active connection while they’re waiting for the response to be generated. Connecting only for the fraction of a second needed to send a prompt or receive the response is one, very effective, way to minimise the exposure time to threats. This disconnect/reconnect happens too quickly for humans to be aware of any delay, and LLMs have very few use cases which are time critical on the level of microseconds. It’s not the only layer of security to apply, and there are many other ways it can be applied, but it’s definitely one worth thinking about. It sounds like science fiction, but with all the hype around AI, the danger of ‘kidnapping’ is worth talking about. It goes like this - companies who can afford to build their own internal LLMs throw all of their valuable IP (intellectual property, covering everything from trade secret designs to marketing campaigns and product strategies) into the model for it to generate relevant responses. Essentially, while it’s still a non-sentient AI model, it’s also a repository of all the most valuable information the company has. This makes it a fantastic target for a criminal attacker, or even an unethical competitor. If it can be poisoned to generate bad responses, cloned to give the attacker more insider knowledge than they know what to do with, or even somehow locked down for ransom, then it can do a huge amount of damage to the company. Companies struggle to secure their systems, with regular headlines showing large enterprises being breached. An LLM model, containing everything about the company, is going to quickly become a favoured target for virtual ‘kidnapping.’ Whether it’s a case of adapting ransomware to encrypt models, exfiltrating them and destroying the local copy, or subverting them into an insider threat (say, if the LLM has been integrated with the company’s invoicing and finance systems without effective human oversight), the potential for harm is dramatic. Making it worse, the value of these models will come from their availability and accessibility within the company. If no one can use them, they’re nothing but a resource sink. They must be used effectively to have value, and that means they must be accessible. How do we match the need for availability with the need for security on these systems? Is Software Security Enough? Maybe software-defined networks could be a solution? The problem is that any software solution is inherently vulnerable to compromise. And if we’re talking about an LLM which has been integrated with administrative systems (this is already being done, the ethics and risks of this are a whole article in themselves - maybe a book), then it would be simplicity itself for a malicious party to prompt the AI to create a way in for further attacks. Software solutions, and classic devices such as firewalls and data diodes, definitely have a place in a secure AI system design, but something with so much value to a company justifies a much more absolute approach. If we go old-school, what we’d talk about as the last resort for any attack is pulling the plug. Ultimately, unless an attacker can change the laws of physics (or is sitting inside the data centre, which is unlikely) unplugging the network is pretty unbeatable. The problem is that unplugging and reconnecting the network takes time - making a phone call to your network engineer to plug it in or out every time you want to send an authorised prompt doesn’t seem a great option. The Goldilock FireBreak I recently came across a security device which impressed me, which doesn’t happen often these days. It wasn’t because of its brand new quantum dark web AI firewall technology, as so many vendors advertise these days. Instead it was because it’s a company which has taken a very old idea, thought about it, and brought it bang up to date in a way that opens up a lot of possibilities for secure network topologies. The approach is simple, and pretty unbeatable (obviously nothing is 100% secure, but the attack vectors against a physical disconnect are vanishingly sparse). Essentially the FireBreak (as opposed to firewall, of course) unplugs the network cable on command - or plugs it back in. It’s not quite having someone on-call in the data centre to manipulate cables on demand, but that’s an easy way to think about it - except the person can plug or unplug the cables in a fraction of a second in response to prompts from any method you choose. FireBreak Often management networks are separated only by a firewall, whereas FireBreak itself has a control plane completely out-of-band from the network. I can easily imagine tying together FireBreak with your ticketing system to only physically connect the management planes during defined change windows, making them unavailable even to the most privileged attacker without going through a documented process. “Operating in a so-called ‘post-breach era’, the security industry seems resigned to the idea that cyber-attacks are a matter of ‘when’ and not ‘if’. At Goldilock, we wanted to find a way to empower individuals and organisations to take more accountability and control over what happens to their data. Our flagship product, FireBreak, was born out of this idea. Its hardware-based approach enables users to physically segment all digital assets, from LLMs to entire networks, remotely, instantly and without using the internet. “Operating in a so-called ‘post-breach era’, the security industry seems resigned to the idea that cyber-attacks are a matter of ‘ when’ and not ‘ if’ . At Goldilock, we wanted to find a way to empower individuals and organisations to take more accountability and control over what happens to their data. Our flagship product, FireBreak, was born out of this idea. Its hardware-based approach enables users to physically segment all digital assets, from LLMs to entire networks, remotely, instantly and without using the internet. when’ if’ This allows users to physically connect and disconnect when required, from wherever they are in the world, hiding any assets from view and enhancing their existing depth of defence, including against AI, which is essentially just a type of software. This allows users to physically connect and disconnect when required, from wherever they are in the world, hiding any assets from view and enhancing their existing depth of defence, including against AI, which is essentially just a type of software. Ultimately, we want people to rethink what needs to be kept online and see a move away from the "always-on" model that can leave sensitive data exposed. Because any data kept online is at risk of being breached. With more and more companies training their own LLM models in-house, a new realm of security considerations needs to be made. Network segmentation should be part of a holistic cybersecurity strategy that keeps bad actors out and ensures they can be stopped in their tracks if they do breach external parameters." Ultimately, we want people to rethink what needs to be kept online and see a move away from the "always-on" model that can leave sensitive data exposed. Because any data kept online is at risk of being breached. With more and more companies training their own LLM models in-house, a new realm of security considerations needs to be made. Network segmentation should be part of a holistic cybersecurity strategy that keeps bad actors out and ensures they can be stopped in their tracks if they do breach external parameters." -- Tony Hasek, CEO and Co-Founder of Goldilock -- Tony Hasek, CEO and Co-Founder of Goldilock So how does this help with the LLM kidnappings? LLM models are big. No one is going to clone one in seconds. They can also be used asynchronously - they don’t depend on a constant connection to generate a response to a prompt and could easily reconnect and send it when finished instead of working in an interactive session. There is no need for a user to have an active session to the LLM while they’re generating a prompt. There’s no need for them to have an active connection while they’re waiting for the response to be generated. Connecting only for the fraction of a second needed to send a prompt or receive the response is one, very effective, way to minimise the exposure time to threats. This disconnect/reconnect happens too quickly for humans to be aware of any delay, and LLMs have very few use cases which are time critical on the level of microseconds. It’s not the only layer of security to apply, and there are many other ways it can be applied, but it’s definitely one worth thinking about.

Lock Up Your LLMs: Pulling the Plug

আপনার LLMs লক আপ করুন: প্লাগ টানা

Sperren Sie Ihre LLMs: Den Stecker ziehen

Bloquee sus LLM: desconecte

Verrouillez vos LLM : débranchez-vous

अपने एलएलएम को लॉक करें: प्लग खींचना

LLM をロックする: プラグを抜く

LLM 잠그기: 플러그 뽑기

Bloqueie seus LLMs: Puxando o Plugue

Заблокируйте свои программы LLM: выдерните вилку

Yüksek Lisans Derecelerinizi Kilitleyin: Fişi Çekmek

Khóa LLM của bạn: Rút phích cắm

锁定你的法学硕士 (LLM)：拔掉插头

Life in the Next 100 Years According to Jail-Broken Claude Sonnet 3.5

A Voice Controlled Website With AI Embedded in Chrome

ক্রোমে এম্বেডেড এআই সহ একটি ভয়েস নিয়ন্ত্রিত ওয়েবসাইট

Eine sprachgesteuerte Website mit in Chrome eingebetteter KI

Un sitio web controlado por voz con IA integrada en Chrome

Un site Web à commande vocale avec IA intégrée dans Chrome

क्रोम में ए.आई. एम्बेडेड के साथ एक आवाज नियंत्रित वेबसाइट

Chrome に AI が埋め込まれた音声制御のウェブサイト

Chrome에 AI가 내장된 음성 제어 웹사이트

Um site controlado por voz com IA incorporada no Chrome

Веб-сайт с голосовым управлением и искусственным интеллектом, встроенным в Chrome

Chrome'a Yerleştirilmiş Yapay Zeka ile Sesle Kontrol Edilen Bir Web Sitesi

Trang web được điều khiển bằng giọng nói với AI được nhúng trong Chrome

Chrome 中嵌入 AI 的语音控制网站

SENTIENT SOLUTIONS

sentientsolutions.io

-- employees

Since n.d.

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SENTIENT SOLUTIONS

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SENTIENT SOLUTIONS

sentientsolutions.io

-- employees

Since n.d.

COMPANY RANKING

SENTIENT SOLUTIONS

EVERGREEN INDEX #586

RELATED COMPANIESRANK

STORIES ABOUT SENTIENT SOLUTIONS

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