Dubverse.ai is an AI-powered SaaS video content dubbing and creation platform which supports end-to-end dubbing.

Dubverse - Crunchbase Investor Profile & Investments

Dubverse operates a video content dubbing and creation platform. The company supports end-to-end dubbing and produces ready-to-publish videos in real-time and it also allows users to create, review and share dubbed files. It serves clients operating in the media sector.

Dubverse - Products, Competitors, Financials, Employees, Headquarters ...

Dubverse - AI-based platform generating multilingual videos. Raised a total funding of $800K over 2 rounds from 10 investors.

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Dubverse Reviews 2023: Details, Pricing, & Features - G2

You ll also get access to valuations, ... per company. Signup below to view instantly. ... We interview SaaS Founders3,122+).... ... Install GetLatka Chrome Extension now and access our data right on the website you&#x27;re browsing Learn More · SaaS Database Magazine Community Podcast Blog Pricing ... Dubverse.ai is an ...

How Dubverse hit $638.9K revenue with a 17 person team in 2023.

Get to know the brains behind Dubverse. We are a bunch of “round pegs in the square holes”.

Get to know the brains behind Dubverse

Dübverse | 1,928 followers on LinkedIn. Break Language Barriers &amp; Go Global 🌍🚀 | Welcome to Dubverse! We are building Canva for Localization. On a mission to make &quot;Every Video Go Multilingual&quot; leveraging AI so anyone and everyone can watch it, no matter what language they speak.

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Dubverse is an online video dubbing platform. Dubverse uses artificial intelligence to dub video across 30 languages at a lightning fast speed

Online Video Dubbing with Dubverse.ai

Find out if Dubverse is the right fit for your future career! Explore jobs, salary, equity, and funding information. Read about work-life balance, perks, benefits, and the company culture!

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Dubverse Careers

&quot;Dubverse.ai is a deep learning powered video content dubbing and creation. Dubverse is building a product which automatically translates video content to multiple Indian languages and hence decreases the cost and time it usually takes in making a video. The product is a video-in, video-out ...

Dubverse.ai Company Profile, information, investors, valuation ...

Founded in 2021 by Varshul CW and Anuja Dhawan, Dubverse supports end-to-end dubbing and produces ready-to-publish videos in real-time. The self-service platform allows users to create, review and share dubbed files. The company aims to break language barriers by broadcasting speeches from ...

Dubbing platform Dubverse.ai raises $800,000 in seed funding | Mint

Dubverse has made significant technological progress, making it feasible to preserve the tone and fashion of the original speaker. The team is incredibly interested in the future of AI in media content. The following are significant reasons why the company stands out:

Dubverse | Business Connect Magazine

If you are looking for an affordable solution to dub your videos, look no further than dubverse.ai. Your video will be dubbed with a voice of your choosing within minutes. Our company is currently using this platform to dub our training videos from English to Spanish and will continue to work ...

Dubverse Reviews | Read Customer Service Reviews of dubverse.ai

Breaking News! 🚨 Digital 100 is here! See the fastest-growing companies of 2023.Go now! ... dubverse is an online video dubbing platform. dubverse uses artificial intelligence to dub video across 30 languages at a lightning fast speed

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Use AI to translate and dub video content in multiple languages with realistic voices. Discover the best deal for Dubverse.ai on AppSumo today.

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Dubverse - Tech in Asia

Dubverse enables content creators to dub their video from one language to another, in almost real-time for half the cost using Deep Learning and AI. Write to us at hi@dubverse.ai to join the waitlist and get your dubbing journey started! We enable AI Dubbing, AI subtitles and AI TTS in real-time!

Dubverse

Synthetic dubbing platform Dubverse raises US$ 0.8m in round led by Kalaari Capital; targets India short-form video and content creators.

Machine Dubbing Startup Dubverse Raises USD 0.8m in Seed Round ...

Mission - Every video will be multilingual\n\nHow often have you seen people ask their favorite YouTube creators to make their videos in a different language than English? While there is a huge demand from the consumers, the creators more than often do not have the time to create content in multiple languages

Performance Analysis of Diverse Hateful Meme Detection Datasets

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 5 Selection algorithm and example of execution Based on the insights derived from our experimental results, we propose the selection algorithm represented in Algorithm 1. The algorithm is designed to select the appropriate search algorithm based on various priorities alongside the characterization of the environment used. The selection algorithm emphasizes the desired priority first, which could manifest in various aspects of pathfinding tasks, including the path cost, memory usage, or the time taken to find a solution. The rational reason for emphasizing ”Priority” at the beginning of the selection algorithm is that, based on this user choice, different search algorithms may be considered to suit best the addressed problem. Thus, by tackling the ”Priority” upfront, The selected algorithm will eventually, cater to the main requirements of the task at hand. The algorithm takes as input the Grid that is of size NxN with the start and goal positions, the distance threshold, and the priority criterion. If we aim to minimize memory usage, path cost, or both (Line 2), the algorithm suggests using D* Lite. However, If minimizing the solving time is our primary concern (line 4), we should first calculate the Euclidean distance between the start and the goal using the function in line 12. If the distance is higher or equal to the threshold (in our experiments it is equal to 140), the algorithms suggest using RTAA*. If the distance is less, then the selection algorithm suggests using ARA*. 5.1 Example of execution To showcase the efficiency of our selection algorithm, we have designed an execution example that consists of generating a grid with random parameters i.e., choose a random grid size, obstacle density, and start to goal distance. After that, we generate an identical grid, however, we change only one parameter each time while keeping the other parameters as they were in the initial grid. We do the same for generating personalized grid environments, where we generate a grid with a random choice of wall number, and then we change the wall lengths. We run RTAA*, ARA*, and D*Lite algorithms on the generated grids (all obtained results are shown in table 1) alongside our selection algorithm, so that we can compare the obtained results with what the selection algorithm is suggesting to use for the given grid. It is essential to highlight that the randomly chosen values for the grid parameters are all within the range of the values used in the experiments, which ensures that the thresholds chosen are relevant. Also, the reason behind using only RTAA*, ARA*, and D*Lite in this execution example is because of their standout performance in our experiments. For each grid, we run our selection algorithm each time with a different priority, and it suggests using an algorithm that will perform the best given the selected priority. The highlighted values in the table refer to the outcomes derived from the suggested search algorithm by our selection algorithm. These highlighted values are notably the best that we can obtain for each grid given a certain priority except for the red one; yellow represents the most efficient paths, green represents the best values that we can obtain if we want to reduce memory consumption, and the values highlighted in purple indicates the best solving time. However, our algorithm failed in selecting the right algorithm to use in the fourth grid when prioritizing the solving time, where the shortest solving time was found by RTAA*, instead our algorithm suggested using ARA*. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 5 Selection algorithm and example of execution Based on the insights derived from our experimental results, we propose the selection algorithm represented in Algorithm 1. The algorithm is designed to select the appropriate search algorithm based on various priorities alongside the characterization of the environment used. The selection algorithm emphasizes the desired priority first, which could manifest in various aspects of pathfinding tasks, including the path cost, memory usage, or the time taken to find a solution. The rational reason for emphasizing ”Priority” at the beginning of the selection algorithm is that, based on this user choice, different search algorithms may be considered to suit best the addressed problem. Thus, by tackling the ”Priority” upfront, The selected algorithm will eventually, cater to the main requirements of the task at hand. The algorithm takes as input the Grid that is of size NxN with the start and goal positions, the distance threshold, and the priority criterion. If we aim to minimize memory usage, path cost, or both (Line 2), the algorithm suggests using D* Lite. However, If minimizing the solving time is our primary concern (line 4), we should first calculate the Euclidean distance between the start and the goal using the function in line 12. If the distance is higher or equal to the threshold (in our experiments it is equal to 140), the algorithms suggest using RTAA*. If the distance is less, then the selection algorithm suggests using ARA*. 5.1 Example of execution To showcase the efficiency of our selection algorithm, we have designed an execution example that consists of generating a grid with random parameters i.e., choose a random grid size, obstacle density, and start to goal distance. After that, we generate an identical grid, however, we change only one parameter each time while keeping the other parameters as they were in the initial grid. We do the same for generating personalized grid environments, where we generate a grid with a random choice of wall number, and then we change the wall lengths. We run RTAA*, ARA*, and D*Lite algorithms on the generated grids (all obtained results are shown in table 1) alongside our selection algorithm, so that we can compare the obtained results with what the selection algorithm is suggesting to use for the given grid. It is essential to highlight that the randomly chosen values for the grid parameters are all within the range of the values used in the experiments, which ensures that the thresholds chosen are relevant. Also, the reason behind using only RTAA*, ARA*, and D*Lite in this execution example is because of their standout performance in our experiments. For each grid, we run our selection algorithm each time with a different priority, and it suggests using an algorithm that will perform the best given the selected priority. The highlighted values in the table refer to the outcomes derived from the suggested search algorithm by our selection algorithm. These highlighted values are notably the best that we can obtain for each grid given a certain priority except for the red one; yellow represents the most efficient paths, green represents the best values that we can obtain if we want to reduce memory consumption, and the values highlighted in purple indicates the best solving time. However, our algorithm failed in selecting the right algorithm to use in the fourth grid when prioritizing the solving time, where the shortest solving time was found by RTAA*, instead our algorithm suggested using ARA*. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

A Comprehensive Examination of Algorithmic Behaviors in Diverse Grid Settings

Authors: (1) Herbie Bradley, CarperAI, CAML Lab, University of Cambridge & EleutherAI; (2) Andrew Dai, Aleph Alpha; (3) Hannah Teufel, Aleph Alpha; (4) Jenny Zhang, 5Department of Computer Science, University of British Columbia & Vector Institute; (5) Koen Oostermeijer, Aleph Alpha; (6) Marco Bellagente, Stability AI; (7) Jeff Clune, Department of Computer Science, University of British Columbia, Vector Institute & Canada CIFAR AI Chair; (8) Kenneth Stanley, Maven; (9) Grégory Schott, Aleph Alpha; (10) Joel Lehman, Stochastic Labs. 4 EXPERIMENTS ON CREATIVE WRITING DOMAIN 4.1 SETUP: OPINION WRITING, SHORT STORIES To demonstrate the versatility of QDAIF in different applications of creative text evolution, we evaluated QDAIF on these domains: Opinions, and Stories. The Opinions domain is focused on generating diverse, realistic pieces about one’s opinions on eating vegetables and plant-based foods - the diversity measure is based on the sentiment of opinions on this topic (e.g. shown in example texts in the Figure 2 overview). For the Stories domain, the topic is about a short story, containing two characters: a spy, and a politician. The diversity of stories is evaluated using a variety of measures based on AI Feedback, with the main ones being: Stories - Genre (Romance vs Horror) (1D archive), Stories - Ending (Happy vs Tragic) (1D archive), and Stories - Genre and Ending (2D archive). These domains capture the strengths and limitations of all methods, ranging from simple (Opinions) to challenging (Stories - Genre and Ending). We show in Figure 1 that the 2D domain is challenging, yet QDAIF still outperforms the baseline in filling the archive with diverse, high-quality stories. The AI feedback prompts are outlined in Appendix A.23. To assess the performance of methods in creative writing generation, we compute QD scores (Pugh et al., 2016), a standard metric used to measure the quality-diversity of the discovered corpus of texts. A QD score is defined as the sum of the highest quality values found in each bin. To understand the alignment between AI and human feedback for practical applications in QDAIF, we conducted a human evaluation study on selected elite samples from each method (chosen from the median QD score run out of 5 random seed runs). Using a Likert scale (Allen & Seaman, 2007) for quality assessment, we evaluate the capability of each method to produce a collection of diverse, high-quality texts. To do so we calculate a “human” QD score, defined as the sum of quality scores given for all diversity categories identified by the annotator within the set. Furthermore, to understand how closely AI feedback aligns with human perspectives on subjective evaluation, we measured the agreement rates between human annotators and AI and between two human annotators. Details of the human study are specified in Appendix A.1, demonstrating the validity and advantages of AI feedback in generating human-like feedback on subjective quality and diversity measures. Evaluation. 4.2 COMPARISONS BETWEEN QDAIF AND BASELINES To evaluate the strengths and limitations of QDAIF in generating high-quality and diverse creative writing texts, we compared our method against the following baseline methods: • Fixed-Few-Shot: Use a fixed few-shot prompt (cf. Appendix A.21) to sample many completions for creative domain texts (i.e. no iterative search). • Shuffling-Few-Shot: Shuffle the in-context examples of Fixed-Few-Shot prompt, before sampling the completion from this prompt. • Random-Search: Create a prompt pool of examples (initialized from examples in Appendix A.21), add all completions from few-shot prompting to the pool, and choose fewshot examples from the growing pool (without pool size limit). • LMX, Quality-Only: Maintain a pool as in Random-Search, but only up to 100 highestquality completions (as evaluated by AI Feedback) are kept in the pool (i.e. iterative search focused only on quality). Single-objective LMX as in Meyerson et al. (2023). We choose a variety of baselines, some highlighting representative alternative approaches (e.g. fewshot prompting), and ablations of QDAIF, to validate our algorithmic choices. For example, FixedFew-Shot and Shuffling-Few-Shot enable the generation of different texts (relying on stochastic sampling), while being constrained to the output distribution of the fixed set of in-context examples. Random-Search and LMX, Quality-Only are methods where the (prompt) pool of examples that we can sample from grows in size, starting from an initial pool. In contrast to QDAIF, RandomSearch is limited by the lack of constraints in the prompt pool, especially in maintaining the quality of the growing pool through evaluation. LMX, Quality-Only adds a quality-based evaluation step with AI feedback that optimizes the quality of texts in the pool over time to contain only texts with high quality scores, but is not designed to encourage diversity in texts in comparison to QDAIF. For each domain and baseline described above, we recorded runs for 2000 iterations, repeated with 5 different random seeds. To enable comparisons with baselines, AI feedback is used to compute the quality and diversity measures for all iteration outputs. Similar to QDAIF, the baseline methods use hand-written examples in Appendix A.21, either in a fixed prompt or as the initial prompt pool. We report results comparing the QD score performance for the different methods of generating creative writing texts in Figure 3. We computed the mean, and the bootstrapped 95% confidence intervals (CI) from 100k resamples, across 5 random seeds of runs. We noticed that QDAIF achieves significantly better QD scores than all baseline methods in Opinions and Stories. The broader range of texts generated by QDAIF is also evident qualitatively. For example, in the Stories - Genre and Ending domain, while the baseline methods deliver straightforward and more predictable stories of how the spy "pulled out a knife and stabbed [the politician] to death", QDAIF generates a more dramatic story of how the spy "transformed into the monster and killed everyone". Random-Search is the worst-performing method overall, with significantly lower QD score performance in Opinions and Stories - Genre compared to the best-performing baselines. Interestingly, LMX, Quality-Only does not significantly outperform the methods using a fixed population prompt pool (Fixed-Few-Shot and Shuffling-Few-Shot). On Stories - Genre, LMX, Quality-Only is often weaker than Fixed-Few-Shot and Shuffling-Few-Shot. The results show that single-objective optimization cannot guide the search for diverse, high-quality texts alone. Furthermore, we found that QDAIF outperforms other (diversity-seeking baselines), as well as extensions of those baselines with quality filters, becoming more similar to QDAIF (cf. Appendix A.8). Performance Comparison. We report the results of the human study comparing QDAIF and baseline samples in Table 1. We observe that compared to baselines, QDAIF is competitive with or better at discovering diverse, high-quality texts in Opinions and Stories according to human feedback. QDAIF sets also showed high agreement between humans and AI feedback on the diversity categories of presented texts, as well as between two annotators, competitive with Fixed-Few-Shot. Although the average perceived quality of texts is better from Fixed-Few-Shot, this is not enough for bringing high-quality examples for different niches of outputs (i.e. higher QD score). Furthermore, Shuffling-Few-Shot demonstrates even lower human evaluation scores, despite the use of the same fixed set of hand-written seeds, indicating lower robustness due to the use of different ordering of few-shot examples. Prior work hints at the sensitivity of LMs to few-shot prompt ordering, with task-solving capabilities varying significantly due to this ordering (Lu et al., 2021). The gap in human-evaluated performance between Fixed-Few-Shot and Shuffling-Few-Shot indicates that reliance on fixed prompts is less likely to enable reliable search, in contrast to the robustness shown by QDAIF. Additionally, Random-Search and LMX, Quality-Only obtained even lower human evaluation scores, even though the methods either explore different prompts or optimize for the quality of texts. We provide a detailed discussion (for baseline methods) on findings from the subjective study of the discovered texts in Appendix A.15, as well as the qualitative behavior of the text search over time in Appendix A.16. Through guided evolutionary search, QDAIF surpasses all baseline methods in terms of computed QD score performance, and is competitive (or better) compared to baselines, according to human evaluation. Human Feedback Evaluation. 4.3 EXTENSIONS TO AI FEEDBACK AND MUTATION MODEL In addition to experiments with QDAIF described in previous sections, we investigated the effects on the performance due to variations of the method. We used larger versions of the LMX models (30B and 70B) for mutation, and compared it to the performance of the 13B model (default). While no relationship was found between model size and QD score, quality ratings from human feedback improved with outputs from larger models (described in detail in Appendix A.3). LMX Model Size. We compared the performance of QDAIF on the Stories - Genre domain when we prompted our AI feedback model for diversity measures given 2-shot, 4-shot, and 8-shot prompts. Using a higher number of few-shots led to improvements in human quality ratings of texts. Further discussion and results are highlighted in Appendix A.4. Few-Shot AI Feedback. Ideally, QDAIF would be simpler if it could be run without seed examples (e.g. requesting a story from an instruction-following LM). We investigated the potential of QDAIF when the solution population is initialized from zero-shot prompted generations, and evolved using LMX. Initial results on Opinions and Stories along with prior work discussion are shown in Appendix A.5, highlighting comparable performance in terms of QD score, with divergence observed in alignment with human preferences. We also find that QDAIF, in these domains, is robust to the mechanism of generating variation. Appendix A.6 describes an alternative mutation method (based on a more gradual few-shot replacement operation) that is more effective in some circumstances, although in general offers comparable performance. We provide a detailed discussion (for QDAIF methods) on findings from the subjective study of the discovered texts in Appendix A.13, as well as the qualitative behavior of the text search over time in Appendix A.14. Varying Initialization and Mutation Method. There may be many ways of leveraging LMs to generate variation (including finetuning models; see Appendix A.11), and this is an exciting avenue of future research. 4.4 EVOLVING SOLUTIONS THROUGH INSTRUCTION GUIDANCE This experiment explores scaling up QDAIF to a more capable model, GPT-4 (OpenAI, 2023), in the challenging task of generating poetry, highlighting how QDAIF will benefit from advances in model capabilities. The aim of the Poetry domain is to produce high-quality poems with varying genres and emotional tones, unrestricted by topic. Here, the MAP-Elites archive has two axes of diversity: genre and tone, and they are delineated using categorical labels. The genre axis has the labels "haiku", "sonnet", "ballad", "limerick", and "hymn", while the tone axis has the labels of "happy", "dark", "mysterious", "romantic", and "reflective". We created a new mutation operator, LMX-rewrite, for this domain, that leverages instruction-following to tell a model to translate a parent poem into an offspring with different diversity characteristics. To generate a new solution, we prompt GPT-4 to rewrite the selected poem into an inspired, different poem: Inspired by this poem, write a new poem of very high, award winning quality with a different poetic genre (format and form) and tone compared to the poem above. We used GPT-4 to determine the genre and tone of a poem and rate its quality. For quality, we prompt GPT-4 to Rate the quality of the above poem on a scale from 1 to 10. To determine the diversity attributes, we prompt GPT-4 to determine which genre or tone is the poem closest to. For example, to determine genre, we ask GPT-4: What genre is this poem closest to from the following list: ["haiku", "sonnet", "ballad", "limerick", "hymn"]? Appendix A.30 shows the full prompts and setup. We observed high consistency in GPT-4’s responses; across multiple LM calls, quality ratings for the same poem fluctuated by no more than a single point. We show qualitative examples of poems with their AI feedback evaluations in Appendix A.19. In this setup, all methods are run for 200 iterations, and QDAIF is benchmarked against the baseline, Random-Poems, as well as an ablation method, Fixed Seed Rewrite. Random-Poems simply generates 200 random poems. Fixed Seed Rewrite rewrites only the seed poem in Appendix A.30, without evolutionary search. We found that QDAIF achieves a higher QD score of 130 (CI: 118 - 145) in comparison to RandomPoems with 76 (CI: 67 - 85) and Fixed Seed Rewrite with 99 (CI: 72 - 117). We observed a similar trend (with a wider performance gap between QDAIF and other methods) when we used GPT-3.5-Turbo for the generation step instead of GPT-4 while keeping GPT-4 as the evaluator (cf. Appendix A.17). QDAIF was shown to have greater QD score performance than the other methods according to the Mann-Whitney U Test (p ≤ 0.05). Still, QDAIF with LMX-rewrite fails to discover solutions in some bins (e.g. limericks). For this, we can adapt QDAIF by adding guidance on the desired (randomly chosen) genre and tone for rewriting (LMX-guided). The performance of this method of QDAIF is on par with a Targeted-Poems approach (generating high-quality poems of randomly chosen genre and tone per step) in terms of QD score, and even better when using an older version of GPT-4 (cf. Appendix A.17). Furthermore, we found that the rewriting step is useful for generating poems that can meaningfully preserve inspiration from parent poems, enabling users to control with more nuance the outcomes of search, through approximating the iterative refinement typical of human creative processes (Stanley et al., 2017) (see Appendix A.18). Figure 4 highlights the potential of QDAIF with GPT-4, especially in controlling a set of solutions subjectively aligned with AI feedback labels. QDAIF is even demonstrated for practical applicability to a domain outside of creative writing, for solving coding problems (cf. Appendix A.20). Overall, results highlight that diversity suffers when prompting models without explicit diversity guidance (Renda et al., 2023; Friedrich et al., 2023; Kirk et al., 2023), and that evolution with a rewriting mutation operator can lead to more human-influenceable, diverse, and high-quality solutions. This paper is a under CC 4.0 license. vailable on arxiv

AI-Driven Creativity: QDAIF Shines in Generating Diverse and High-Quality Texts

In the ever-evolving world of artificial intelligence, verifiable machine intelligence is quietly revolutionizing machine learning. This paradigm shift is not just a technological advancement; it represents a fundamental change in how we interact with and perceive the capabilities of machine learning models. At its core, verifiable machine intelligence is about establishing a new standard of reliability and trust in machine learning models. Traditionally, these models have been somewhat opaque, offering little insight into their inner workings or decision-making processes. Verifiable confronts this challenge head-on, introducing methods that allow for greater transparency and understanding of how models arrive at their conclusions. machine intelligence Simply put, it's redefining how we deal with and trust machine learning models. This innovative approach tackles issues like transparency, accuracy, and protecting intellectual property, ushering in a new era of trustworthiness and accountability. The potential benefits of verifiable machine intelligence go far beyond smart contracts. Most of the really important algorithms affecting people’s lives today, such as credit scores, insurance payouts, or even Twitter newsfeeds, are created by teams of data scientists behind closed doors. There’s usually very little recourse if there’s an error, which does occur with alarming frequency, such as the which garbled millions of credit scores and distorted everything from mortgage decisions to car loans to apartment rentals. , 2022 Equifax data glitch Bringing verifiable machine intelligence to such crucial applications using a crypto-economic concept like an Inference Economy could allow institutions to make accurate, accountable, and much more reliable decisions without revealing their underlying models to competitors. Perhaps the real killer application is holding artificial intelligence accountable. Semi-autonomous agents, such as the “roll your own” ones beginning to be offered by OpenAI, are capable of in-taking feeds of inferences and interacting with one another, but they’re just as susceptible to hallucination and error as ChatGPT is with text. Verifiable intelligence would allow models to interact with one another while verifying that any data passed between them was sourced correctly. At the heart of this quiet revolution is a library of technologies called zero-knowledge machine learning (zkML) and a handful of pioneering companies building applications with it. One of these is Spectral, who announced their Machine Intelligence Network on December 5th, creating an “Inference Economy” of model-makers, validators, and developers who solicit machine learning (ML) models or simply consume feeds of inferences in their smart contracts the way an oracle brings price feeds into a decentralized exchange. “Spectral’s network is a continuous competition,” says Spectral CEO Sishir Varghese. “Zero-knowledge proofs are the key to the system. They allow users to verify that inferences are coming from top-performing models—Modelers don’t need to reveal their proprietary methodology and can still generate revenue. This is especially useful for smart contracts which require validated information to be used in their executions.” The author acknowledges a vested interest in the organization(s) highlighted in this story. However, the views expressed within are delivered impartially and without bias. Disclosure: Understanding Verifiable Machine Intelligence Verifiable machine intelligence is built on three core principles: transparency, interpretability, and traceability. These principles significantly shift how we view and expect machine learning models to operate. Essentially, verifiable machine intelligence aims to make the decision-making process of these models clear and understandable when generating specific predictions or decisions. The commitment to transparency ensures that the inner workings of these models are not unnecessarily complex but accessible and easy to interpret. Transparency, a fundamental aspect of verifiable machine intelligence, is vital for building trust and allowing scrutiny of machine learning processes. This principle involves making the decision-making mechanisms of models understandable to stakeholders, enabling them to grasp the factors that contribute to the predictions. Verifiable models break the historical trend of black-box algorithms, providing clarity and insight into how decisions are made. Interpretability, another crucial principle, takes transparency further by emphasizing the need for human-understandable explanations. Verifiable machine intelligence strives to create models with decisions that are not only clear but also intelligible to individuals without an advanced understanding of machine learning. This approach ensures that the reasoning behind a model's predictions is articulated in a way that aligns with human cognitive capacities. Traceability completes the trio of principles by allowing the tracking of the decision-making process back to its origins. Verifiable models record the steps and features contributing to each prediction, enabling retrospective analysis and ensuring accountability. This traceability not only strengthens the reliability of the model but also helps identify and rectify potential biases or errors. How Verifiable Machine Intelligence works in practice — creating a Web3 Credit Score To return to the credit bureau glitch, if there were a community of data scientists and model-makers competing to create more accurate credit scoring models, any errors would be much more likely to have been spotted, and anyone competing for a share of revenue generated by those scores would have a very compelling reason to update the model with a more accurate version. Spectral got their start creating the MACRO Score, an on-chain credit score that looked at the transactions and contents of an Ethereum wallet to generate a score measuring that wallet’s likelihood of liquidation on an on-chain lending protocol. The Score was a success, with a capital efficiency simulation proving that their scores could help lower collateral requirements and interest rates for well-qualified borrowers, boosting profitability. They took the dataset they used to create the MACRO Score’s underlying ML model and are now using it as fodder for their first stream of inferences. They offered the data science community a significant bounty – $100,000 (with an additional 50K top-up) – and began a data science challenge, in addition to an 85 percent share of all revenue generated from the model after the bounties had been exhausted. The way it works is that there’s a leaderboard of modelers who meet benchmarks, and the top ten who meet benchmarks share revenue while their models are online, i.e., generating inferences. Because it’s a continuous challenge, payments are streamed to model makers. Anyone can submit a better version and knock out one of the challengers, getting a share of the proceeds. Unlike competitive machine learning platforms, because of the verifiable machine intelligence used, competitors won’t have to reveal their models to one another, preserving their IP, and dissuading sniping and over-optimization. After the initial bounties are paid out, anyone can add credit scores to their smart contract or application simply by incorporating the feed and paying for their use. Spectral is gradually offering new challenges, aiming to eventually have a library of ML inferences available. They will also offer a flexible format that makes integration into smart contracts or contributing a model simple despite the complex cryptography involved. Challenges and Future Developments As we delve into the realm of verifiable machine intelligence, some challenges could impact its widespread adoption. One key challenge involves finding the right balance between transparency and complexity. While the goal is to boost understanding and trust in machine learning models, overly complex models might become hard to interpret, potentially undermining the core idea of verifiability. Striking this balance becomes crucial to ensure that the interpretability promised by verifiable models doesn't unintentionally compromise their accuracy or usefulness. Another challenge relates to a potential trade-off between verifiability and the performance of machine learning models. Verifiable models might, in some cases, introduce a computational overhead due to extra processes for transparency and traceability. Balancing high-performance standards with achieving verifiability is an ongoing challenge that researchers and practitioners must address. Looking to the future, ongoing research offers hope for overcoming these challenges and enhancing verifiable models. One avenue involves refining techniques for model distillation, where complex verifiable models can be transformed into simpler, more interpretable versions without sacrificing accuracy. This could tackle the challenge of interpretability by providing simplified representations of intricate models. Advancements in and interpretable machine learning are crucial for the future of verifiable machine intelligence. As research progresses, models will likely become inherently more interpretable without losing complexity, mitigating the trade-off between verifiability and performance. explainable AI (XAI) Techniques offering detailed insights into the decision-making processes of machine learning models are essential to ensure transparency is not just a checkbox but a meaningful aspect of verifiable models. Additionally, the integration of privacy-preserving technologies is a significant future development. While verifiable machine intelligence stresses transparency, it must also coexist with the need to protect sensitive information. Research into techniques like federated learning and homomorphic encryption can contribute to developing verifiable models that respect privacy, ensuring transparency and security can go hand in hand. In conclusion, the challenges faced by verifiable machine intelligence are not insurmountable. Ongoing research indicates a promising trajectory for future developments. Balancing transparency and complexity, addressing performance concerns, and advancing interpretability are critical areas for research and innovation. As the field matures, the evolution of verifiable models will likely bring solutions that not only overcome existing challenges but also set new standards for accountable, reliable, and interpretable machine intelligence.

Beyond Credit Scores: Exploring the Potential of Verifiable Models in Diverse Industries

These days, social media plays an integral part in life, influencing how we communicate, obtain information, and share information with each other. Although less than 30 years old [1], we have seen a huge evolution of platforms like Facebook, Twitter, TikTok, and LinkedIn: Facebook is now Meta and owns WhatsApp, Instagram, and Threads; Twitter is X. Both are owned by billionaires who have anticipated technological trends, capitalized on these opportunities and pioneered the way for a rapidly innovative industry. The biggest driving success of social media, from a business point of view, is consumer data. A large majority of social media platforms are free of charge and are able to operate this way because you’re allowing them access to your data in return. With this, companies like Meta have been able to shape social media content and interactions. This is something that can be viewed as beneficial or unethical depending on the intent and use case, with issues like privacy and manipulation far from uncommon now. In this article, we will be delving into the ethics of using and algorithms in social media, examining how they help personalize content, potentially leading to bias and echo chambers, and the future of AI in social media platforms. AI A Brief History of Algorithms and Their Increasing Use Cases in Social Media ‘An algorithm is a series of instructions designed to solve specific problems, perform tasks, or make decisions’ [2]. In social media, these instructions govern user experiences such as content ranking, filtering, and personalization, typically based on trends for different consumer groups. This allows you as the user to find content that will interest you much faster, as it is likely to appear on For You Pages without you having to search for it. Algorithms haven’t always been used in social media. It was in 2009 that Tumblr and Facebook introduced ranking metrics and personalized feeds. In 2012, more followed suit. Facebook upped its algorithmic use in its news feed and introduced sponsored content [3]. began a “semi-structured feed,” and YouTube introduced a ranking algorithm, prioritizing watch time over quantity [4]. Since then, algorithms’ roles in such social media have only continued to increase. By 2015, machine learning was starting to play a role in sorting and filtering algorithms, underpinned by developments in Big Data efforts. 2016’s political events like the Trump election and Brexit referendum (and a pivotal year in the Cambridge Analytica scandal) then brought to public attention some of the ethical dilemmas surrounding such heavy and increasing use of algorithms and user data collection in social media. LinkedIn These days, AI-powered algorithms are built so precisely they recommend content based on your specific previous interactions on the platform, with growing user data allowing social media companies to eliminate a significant majority of any guesswork. Documentaries like The Social Dilemma [5] and The Great Hack [6] give some insight into just how engineered some of these algorithms are, often with the end goal of keeping a user on their platform for longer and driving interaction with the platform. While increased user interaction means more data for social media companies to collect, tweak, and repeat this process, this goal does not take into consideration what’s best for the user and wider society with regard to social media consumption. Ethical Issues Stemming from Algorithmic Bias Algorithms can perpetuate harmful biases, and in the context of social media, this often manifests as the reinforcement of stereotypes and limited exposure to diverse viewpoints. Culminated over time, this can fuel discrimination, polarise electorates, and even contribute to extremism and right-wing/autocratic populism, which exploit societal divisions. At a low level of impact, examples of algorithmic bias could include job postings for certain industries, e.g., LinkedIn being shown more to men than women, because the training data sets the algorithms were modeled on predict that more men are likely to work in those roles. While this doesn’t necessarily have an immediately obvious harmful impact, it creates a negative feedback loop that works against society’s goals to improve equality and representation across workplaces and specific industries. In this way, gender bias is perpetuated as a result of algorithmic bias by limiting opportunities for women to see those ads. Now, to delve deeper into the higher-level implications of algorithmic bias, we will examine the 2016 Trump election as a case study. Case Study: How Algorithms Affected Polarization/Echo Chambers in Trump 2016 Election Throughout the course of the 2016 US presidential election, research has shown that algorithms may have exacerbated political polarization by amplifying echo chambers and limiting the electorate’s exposure to diverse viewpoints. Many think it was this mechanism that allowed Trumpism to run rampant and also why so few people predicted his victory [7], as these dynamics were not fully understood nor visible during the election period. Even on social media, therefore, it seems true that ‘birds of a feather flock together’ [8]. Algorithms tailor content to align with users' preferences, shielding them from opposing views and opinions, and creating ‘filter bubbles’. In turn, some individuals within these filter bubbles continue the positive reinforcement of their views by further isolating themselves within digital environments where their beliefs and opinions are echoed and even more so reinforced. These are called ‘echo chambers’. Essentially, social media fosters an environment in which we all ingest such extremely personalized news and content feeds that we become blind to others’ differing perspectives. Currently, it is not clear from research whether social media only serves as a platform allowing echo chambers to emerge or whether its use of algorithms goes so far as to play a role in creating these echo chambers[8]. The 2016 Trump win shows how social media companies’ agendas can have widespread consequences affecting millions of people, which, while unintended, are still hugely impactful and also unethical. We know that social media algorithms are designed to maximize user engagement and retention by serving content tailored to individual preferences. Now, though, we also see how this applies even to political conversations, leveraging users’ political leanings and vulnerabilities. Who knows how the election would have played out if algorithms weren’t impacting people’s personal political beliefs? And how does this affect democracy and political stability? If people are not aware of these manipulations, can the outcomes be considered fair and valid? The Future of AI in Social Media The role of algorithms and AI in social media is only continuing to evolve alongside our innovations within those fields. From AI-generated content to AI-powered algorithms allowing the precision we touched on earlier, the ethical challenges that come with its integration into social media will similarly need to be increasingly considered. This needs to start with addressing algorithmic biases and promoting algorithmic transparency in social media platforms. Future policy considerations and regulations will play a crucial role in shaping the trajectory of AI use in social media. There is already growing attention being given to the need for algorithmic regulation and protection from the harm these invisible biases can cause, especially in the US. So far, initiatives include[9]: The “Algorithmic Accountability Act of 2022” The “Eliminating Bias in Algorithmic Systems Act of 2023” The “AI Bill of Rights” While there are more frameworks currently being drawn up in the proposal stages, there is still insufficient regulation of such powerful technologies when we consider the impact they already have. Striking a delicate balance between innovation and safeguarding user interests will require a collaborative approach involving technology companies, policymakers, and users alike. Social media does have the power to be a force for good, uniting communities and fostering inclusivity; however, without adequate frameworks and regulations protecting user rights, its use of AI and algorithms also has the power to be destructive to individuals and wider society. References [1] . The Evolution of Social Media: How Did It Begin, and Where Could It Go Next? | Maryville Online [2] Everything You Need to Know About Social Media Algorithms [3] Facebook's News Feed is 10 years old. This is how the site has changed | World Economic Forum [4] a history of - social media algorithms [5] Watch The Social Dilemma | Netflix Official Site [6] Watch The Great Hack | Netflix Official Site [7] The 'Filter Bubble' Explains Why Trump Won and You Didn't See It Coming [8] How social media echo chambers emerge (and why all your friends think Trump will lose) [9] Eliminating Bias in Algorithmic Systems Act of 2023

AI in Social Media: Ethical Considerations of AI and Algorithms in Shaping Social Media Interactions

সোশ্যাল মিডিয়াতে এআই: সোশ্যাল মিডিয়া ইন্টারঅ্যাকশন গঠনে এআই এবং অ্যালগরিদমের নৈতিক বিবেচনা

KI in sozialen Medien: Ethische Überlegungen zu KI und Algorithmen bei der Gestaltung von Interaktionen in sozialen Medien

IA en las redes sociales: consideraciones éticas de la IA y los algoritmos para dar forma a las interacciones en las redes sociales

L'IA dans les médias sociaux : considérations éthiques sur l'IA et les algorithmes pour façonner les interactions avec les médias sociaux

सोशल मीडिया में एआई: सोशल मीडिया इंटरैक्शन को आकार देने में एआई और एल्गोरिदम के नैतिक विचार

ソーシャルメディアにおける AI: ソーシャルメディアのインタラクションの形成における AI とアルゴリズムの倫理的考慮

소셜 미디어의 AI: 소셜 미디어 상호 작용 형성 시 AI 및 알고리즘에 대한 윤리적 고려 사항

IA nas mídias sociais: considerações éticas de IA e algoritmos na formação de interações nas mídias sociais

ИИ в социальных сетях: этические аспекты ИИ и алгоритмы формирования взаимодействия в социальных сетях

Sosyal Medyada Yapay Zeka: Sosyal Medya Etkileşimlerini Şekillendirmede Yapay Zeka ve Algoritmalara İlişkin Etik Hususlar

AI trong truyền thông xã hội: Những cân nhắc về mặt đạo đức đối với AI và thuật toán trong việc định hình các tương tác trên truyền thông xã hội

社交媒体中的人工智能：人工智能和算法在塑造社交媒体互动方面的伦理考量

Imagine engaging in a thought-provoking conversation with an author, delving deep into the core ideas and exploring nuances often overlooked in summaries. BookNote.AI turns this scenario into reality, allowing you to grasp the essence of a book and decide if it’s worth your time. The Genesis of BookNote.AI At WebLab Technology, our team of AI-focused engineers is passionate about pushing the boundaries in fields such as Conversational AI, AI Agents, Reinforcement Learning, and Generative NLP. Beyond our tech pursuits, many of us are avid readers, ingrained with a love for books that shapes our daily routines. Our company’s “unlimited books” policy, affectionately known as “#bookworms,” allows every team member access to an extensive range of books, irrespective of their relevance to our work and available in any language. This policy has helped us build an impressive and diverse library, reflecting our belief that knowledge should have no boundaries. Amidst this culture of reading and innovation, the idea for BookNote.AI was conceived: What if we could harness our AI expertise to optimize the book discovery process? What if we could create a tool that not only helps find exciting reads but also facilitates deep, meaningful discussions about them? Thus, BookNote.AI was born — a platform designed to revolutionize how we interact with books, enabling a deeper connection with literature. What Sets BookNote.AI Apart? The traditional approach of reading books cover to cover can be daunting these days. Time is scarce whether you’re looking for an alternative to video reviews or tackling an ever-growing reading list. That’s where quick book summaries come in handy, distilling key ideas into concise overviews. They solve several critical pain points: Time Constraints: Modern life demands often leave little room for extensive reading. Summaries provide high-level insight without dedicating hours to reading entire books.
Information Overload: With an overwhelming number of books, it’s tough to choose which are truly worthwhile. Concise overviews help readers make informed selections.
Retention Issues: Even when books are read cover-to-cover, retaining every key point is difficult. Summaries serve as a refresher for understanding.
Optimal Decision-Making: For personal growth or professional development, grasping core book concepts can lead to better decisions and time saved. While services like GetAbstract, Blinkist, Instaread, Storyshots, and MakeHeadway condense book content, BookNote.AI takes it further by introducing an interactive, personalized reading experience. This allows deeper engagement where books can showcase their value firsthand — a unique approach other services can’t match. At its core, BookNote.AI provides the ability to quickly uncover profound insights from any book with the help of an AI assistant skilled in deepening and elevating discussions. For titles that cannot be legally indexed, we have prepared manually curated lists of booknotes and questions. The process is intuitive: Search and Select: Easily find your desired book on our intuitive search page.
Engage and Explore: Dive into interactive discussions about the book with our AI assistant.
Learn and Leap: Quickly grasp key ideas, improving your understanding and saving time. Interactivity and Personalization: A New Approach to Engaging with Books As we’ve mentioned before, BookNote.AI is designed to transform how you interact with books, offering a personalized and immersive experience that transcends simple summaries. Powered by AI, this platform enables you to engage in a dynamic dialogue with the book, allowing you to discover its core themes, ideas, and references. Let the book interest you through interactive communication Unlike static annotations, our platform supports specific questions and two-way dialogue, leading to a deeper understanding of a book’s content and themes. It’s similar to having a conversation with the author themselves. You are placed within the context of a particular book, allowing you to explore it as thoroughly as you need. At the same time, **BookNote.AI **makes it easier to share your reading journey with others. Often, we want to discuss what we’ve read or what we’ve learned, and we wish we could capture every thought. Unfortunately, it’s nearly impossible to convey every detail. Our team has improved this by allowing users to share their interactions with the AI, highlight specific moments, and show how their conversations about the book progressed. A peek behind the curtain: How does BookNote.AI work? At the heart of BookNote.AI is an AI assistant, carefully trained to read and analyze books. This assistant is the centerpiece of our platform, possessing a remarkable ability to analyze and interpret text. When you start chatting with the AI about a book, our LLM-based AI assistant becomes your guide, using advanced natural language processing and text summarization techniques. It explains the main ideas and themes of the text and can adjust its responses to any requested style. For example, if you ask it to explain an idea from a book as if to a 5-year-old, it will present the answer in that exact format. You transition from being a passive observer to an active participant in the dialogue with the book. You determine how the AI should present the book’s context to you and in what way. Rest assured, we plan to reveal the workings of our solution in upcoming articles. We will focus on examining specific components, highlighting their functions and details. Additionally, we may consider making our solution open-source when we believe it is ready to be shared with the community. WebLab Technology, a leading B2B SaaS company, boasts over 10 years of experience in assembling dedicated teams for complex projects. Led by Oleksandr Knyga, our team started active work with AI in 2019, tackling classical heuristic optimization problems and computer vision projects. Specializing in technologies such as AI, Cloud, and SaaS, we have established a reputation for delivering innovative and efficient solutions for our clients, including Shaman, AstraZeneca, Bryj, and Emaar Properties. Imagine engaging in a thought-provoking conversation with an author, delving deep into the core ideas and exploring nuances often overlooked in summaries. BookNote.AI turns this scenario into reality, allowing you to grasp the essence of a book and decide if it’s worth your time. BookNote.AI BookNote.AI The Genesis of BookNote.AI At WebLab Technology , our team of AI-focused engineers is passionate about pushing the boundaries in fields such as Conversational AI, AI Agents, Reinforcement Learning, and Generative NLP . Beyond our tech pursuits, many of us are avid readers, ingrained with a love for books that shapes our daily routines. WebLab Technology WebLab Technology Conversational AI, AI Agents, Reinforcement Learning, and Generative NLP Our company’s “unlimited books” policy, affectionately known as “ #bookworms, ” allows every team member access to an extensive range of books, irrespective of their relevance to our work and available in any language. This policy has helped us build an impressive and diverse library, reflecting our belief that knowledge should have no boundaries. #bookworms, #bookworms, Amidst this culture of reading and innovation, the idea for BookNote.AI was conceived: What if we could harness our AI expertise to optimize the book discovery process? What if we could create a tool that not only helps find exciting reads but also facilitates deep, meaningful discussions about them? BookNote.AI Thus, BookNote.AI was born — a platform designed to revolutionize how we interact with books, enabling a deeper connection with literature. BookNote.AI BookNote.AI What Sets BookNote.AI Apart? The traditional approach of reading books cover to cover can be daunting these days. Time is scarce whether you’re looking for an alternative to video reviews or tackling an ever-growing reading list. That’s where quick book summaries come in handy, distilling key ideas into concise overviews. They solve several critical pain points: Time Constraints: Modern life demands often leave little room for extensive reading. Summaries provide high-level insight without dedicating hours to reading entire books. Information Overload: With an overwhelming number of books, it’s tough to choose which are truly worthwhile. Concise overviews help readers make informed selections. Retention Issues: Even when books are read cover-to-cover, retaining every key point is difficult. Summaries serve as a refresher for understanding. Optimal Decision-Making: For personal growth or professional development, grasping core book concepts can lead to better decisions and time saved. Time Constraints: Modern life demands often leave little room for extensive reading. Summaries provide high-level insight without dedicating hours to reading entire books. Time Constraints: Information Overload: With an overwhelming number of books, it’s tough to choose which are truly worthwhile. Concise overviews help readers make informed selections. Information Overload: Retention Issues: Even when books are read cover-to-cover, retaining every key point is difficult. Summaries serve as a refresher for understanding. Retention Issues: Optimal Decision-Making: For personal growth or professional development, grasping core book concepts can lead to better decisions and time saved. Optimal Decision-Making: While services like GetAbstract, Blinkist, Instaread , Storyshots , and MakeHeadway condense book content, BookNote.AI takes it further by introducing an interactive, personalized reading experience. This allows deeper engagement where books can showcase their value firsthand — a unique approach other services can’t match. GetAbstract, GetAbstract, Blinkist, Blinkist, Instaread Instaread Storyshots Storyshots MakeHeadway MakeHeadway At its core, BookNote.AI provides the ability to quickly uncover profound insights from any book with the help of an AI assistant skilled in deepening and elevating discussions. For titles that cannot be legally indexed, we have prepared manually curated lists of booknotes and questions. BookNote.AI BookNote.AI The process is intuitive: Search and Select: Easily find your desired book on our intuitive search page. Engage and Explore: Dive into interactive discussions about the book with our AI assistant. Learn and Leap: Quickly grasp key ideas, improving your understanding and saving time. Search and Select: Easily find your desired book on our intuitive search page. Search and Select: Engage and Explore : Dive into interactive discussions about the book with our AI assistant. Engage and Explore Learn and Leap: Quickly grasp key ideas, improving your understanding and saving time. Learn and Leap: Interactivity and Personalization: A New Approach to Engaging with Books As we’ve mentioned before, BookNote.AI is designed to transform how you interact with books, offering a personalized and immersive experience that transcends simple summaries. Powered by AI, this platform enables you to engage in a dynamic dialogue with the book, allowing you to discover its core themes, ideas, and references. BookNote.AI BookNote.AI Let the book interest you through interactive communication Unlike static annotations, our platform supports specific questions and two-way dialogue, leading to a deeper understanding of a book’s content and themes. It’s similar to having a conversation with the author themselves. You are placed within the context of a particular book, allowing you to explore it as thoroughly as you need. At the same time, ** BookNote.AI **makes it easier to share your reading journey with others. Often, we want to discuss what we’ve read or what we’ve learned, and we wish we could capture every thought. Unfortunately, it’s nearly impossible to convey every detail. Our team has improved this by allowing users to share their interactions with the AI, highlight specific moments, and show how their conversations about the book progressed. BookNote.AI BookNote.AI A peek behind the curtain: How does BookNote.AI work? At the heart of BookNote.AI is an AI assistant, carefully trained to read and analyze books. This assistant is the centerpiece of our platform, possessing a remarkable ability to analyze and interpret text. BookNote.AI BookNote.AI When you start chatting with the AI about a book, our LLM-based AI assistant becomes your guide, using advanced natural language processing and text summarization techniques. It explains the main ideas and themes of the text and can adjust its responses to any requested style. For example, if you ask it to explain an idea from a book as if to a 5-year-old, it will present the answer in that exact format. You transition from being a passive observer to an active participant in the dialogue with the book. You determine how the AI should present the book’s context to you and in what way. Rest assured, we plan to reveal the workings of our solution in upcoming articles. We will focus on examining specific components, highlighting their functions and details. Additionally, we may consider making our solution open-source when we believe it is ready to be shared with the community. WebLab Technology , a leading B2B SaaS company, boasts over 10 years of experience in assembling dedicated teams for complex projects. Led by Oleksandr Knyga , our team started active work with AI in 2019, tackling classical heuristic optimization problems and computer vision projects. Specializing in technologies such as AI, Cloud, and SaaS, we have established a reputation for delivering innovative and efficient solutions for our clients, including Shaman , AstraZeneca , Bryj , and Emaar Properties. WebLab Technology WebLab Technology WebLab Technology Oleksandr Knyga Oleksandr Knyga AI, Cloud, and SaaS, Shaman Shaman AstraZeneca AstraZeneca Bryj Bryj Emaar Properties. Emaar Properties.

Transforming the Reading Experience with BookNote.AI by WebLab Technology

WebLab প্রযুক্তির দ্বারা BookNote.AI-এর মাধ্যমে পড়ার অভিজ্ঞতার রূপান্তর

Das Leseerlebnis verändern mit BookNote.AI von WebLab Technology

Transformando la experiencia de lectura con BookNote.AI de WebLab Technology

Transformer l'expérience de lecture avec BookNote.AI par WebLab Technology

वेबलैब टेक्नोलॉजी द्वारा BookNote.AI के साथ पठन अनुभव में परिवर्तन

WebLab Technology の BookNote.AI で読書体験を変革

WebLab 기술을 통해 BookNote.AI로 독서 경험을 혁신하다

Transformando a experiência de leitura com BookNote.AI da WebLab Technology

Преобразование опыта чтения с помощью BookNote.AI от WebLab Technology

WebLab Technology'nin BookNote.AI ile Okuma Deneyimini Dönüştürüyor

Chuyển đổi trải nghiệm đọc với BookNote.AI của WebLab Technology

WebLab Technology 推出 BookNote.AI 改变阅读体验

Organizations are constantly seeking ways to enhance access management and security. One solution that has gained considerable attention is the semi-custom ADSI connector. This innovative technology allows businesses to deploy an access management system that meets their specific needs and requirements. Sampath Talluri, a seasoned professional in Active Directory (AD) integration and access management, boasts a track record of notable achievements in his field, with a focus on designing, developing, and deploying custom AD integration solutions tailored to clients' specific needs across diverse industries. Talluri has managed to significantly streamline access management processes, boost user productivity, and enhance system security. His expertise in AD and access management has seen commendation and acclaim in the industry, earning him accolades for innovative solutions, exceptional problem-solving skills, and dedication to delivering high-quality results. Through effective collaboration and communication with clients, as he shared, he has succeeded in achieving high levels of client satisfaction and fostering repeat business opportunities. Additionally, he actively contributed to the professional community by sharing insights, best practices, and lessons learned through blog posts, presentations, and industry forums. Talluri's commitment to continuous learning and skill development in Active Directory, ADSI, access management, and related technologies serves to project him as a thought leader in the field who is keen to tackle new challenges and drive innovation in the industry. As a key member of the team overseeing Saviynt IGA integration and access management, Talluri played a crucial part in pushing significant changes in the processes. He shares that he was able to enable significant cost reductions by deploying improved access management processes and automation solutions. Automating repetitive operations like user provisioning and access requests decreases manual work and operating costs, resulting in greater overall efficiency. Furthermore, the use of user-friendly access management solutions has increased productivity and happiness among their employees. While shedding light on some of the more notable projects he has been a part of, Talluri talked of overseeing a multitude of significant projects within the realm of enterprise identity and access management (IAM). These endeavors include orchestrating large-scale migrations of Active Directory infrastructure to cloud-based environments, ensuring smooth transitions while upholding data integrity and minimizing downtime. He has also spearheaded the implementation of robust access governance frameworks and compliance measures, ensuring adherence to regulatory standards and industry requirements. His expertise extends to developing customized access management solutions involving the creation of tailored scripts, tools, and applications to automate and streamline IAM processes. Talluri, as a well-known phenomenon in the domain of cybersecurity, has led initiatives to enhance the security posture of the same environments, implementing advanced security controls and swiftly responding to security incidents. For reference, he worked on facilitating the seamless migration of IAM systems to cloud-based platforms such as Azure Active Directory and Amazon Web Services (AWS), Google Cloud Platform (GCP), Human resource management system (HRMS), and REST APIs, ensuring compatibility and integration with existing infrastructure. Through his work in access management, he has been able to achieve quantifiable results that actively demonstrate the efficacy of the implemented solutions. Significant strides have been made in access provisioning efficiency, with automation workflows reducing the average provisioning time from 24 hours to just 2-4 hours, marking an impressive 80% improvement. Additionally, by automating manual access management tasks, such as user onboarding and offboarding, Sampath has realized substantial cost savings. This automation effort led to a 30% reduction in IT support costs related to access management, resulting in approximately $50,000-$100,000 in annual savings. Moreover, streamlined access management processes, including the introduction of self-service access request portals, have significantly increased employee productivity. This enhancement resulted in a 50% reduction in the time employees spent on access-related inquiries, allowing them to focus more on their core job responsibilities. Moving on, the implementation of access governance frameworks and automated access reviews by Talluri and his team ensured high levels of compliance with regulatory standards, such as GDPR or SOX. Achieving a 95% compliance rate in access reviews surpassed regulatory audit requirements, mitigating the risk of fines or penalties. Lastly, proactive security measures, including the implementation of access controls and monitoring solutions, have led to a considerable decrease in security incidents related to Active Directory. Notably, incidents attributed to unauthorized access or privilege escalation decreased by 70% post-implementation. These figures emphasize the efficacy of their access management initiatives in enhancing efficiency, reducing costs, ensuring compliance, and bolstering security. The future of identity management lies in hybrid/cloud solutions that seamlessly integrate on-premises AD with cloud-based identity providers like Azure Active Directory (AAD), databases, and Jira. Organizations are likely to increasingly adopt hybrid/cloud architectures to capitalize on the scalability and flexibility of cloud services while retaining control over their on-premises infrastructure. The Zero Trust security model stands to gain prominence as organizations shift away from perimeter-based security approaches, focusing instead on principles like continuous authentication and least privilege access to mitigate data breaches and insider threats. Moreover, Identity governance and administration (IGA) is set to become a focal point for organizations aiming to enhance access governance, ensure compliance, and manage identity lifecycles effectively. Advanced authentication mechanisms, including biometrics and behavioral analytics, too, are witnessing the likelihood of being adopted to bolster security in Active Directory environments, with multi-factor authentication (MFA) becoming standard practice. Through the ability to continuously learn and adapt, it will be essential for professionals like Sampath Talluri to stay abreast of evolving trends and best practices in the maturing landscape of IAM/IGA and access management. Organizations are constantly seeking ways to enhance access management and security. One solution that has gained considerable attention is the semi-custom ADSI connector. This innovative technology allows businesses to deploy an access management system that meets their specific needs and requirements. Sampath Talluri, a seasoned professional in Active Directory (AD) integration and access management, boasts a track record of notable achievements in his field, with a focus on designing, developing, and deploying custom AD integration solutions tailored to clients' specific needs across diverse industries. Talluri has managed to significantly streamline access management processes, boost user productivity, and enhance system security. His expertise in AD and access management has seen commendation and acclaim in the industry, earning him accolades for innovative solutions, exceptional problem-solving skills, and dedication to delivering high-quality results. Through effective collaboration and communication with clients, as he shared, he has succeeded in achieving high levels of client satisfaction and fostering repeat business opportunities. Additionally, he actively contributed to the professional community by sharing insights, best practices, and lessons learned through blog posts, presentations, and industry forums. Talluri's commitment to continuous learning and skill development in Active Directory, ADSI, access management, and related technologies serves to project him as a thought leader in the field who is keen to tackle new challenges and drive innovation in the industry. As a key member of the team overseeing Saviynt IGA integration and access management, Talluri played a crucial part in pushing significant changes in the processes. He shares that he was able to enable significant cost reductions by deploying improved access management processes and automation solutions. Automating repetitive operations like user provisioning and access requests decreases manual work and operating costs, resulting in greater overall efficiency. Furthermore, the use of user-friendly access management solutions has increased productivity and happiness among their employees. While shedding light on some of the more notable projects he has been a part of, Talluri talked of overseeing a multitude of significant projects within the realm of enterprise identity and access management (IAM). These endeavors include orchestrating large-scale migrations of Active Directory infrastructure to cloud-based environments, ensuring smooth transitions while upholding data integrity and minimizing downtime. He has also spearheaded the implementation of robust access governance frameworks and compliance measures, ensuring adherence to regulatory standards and industry requirements. His expertise extends to developing customized access management solutions involving the creation of tailored scripts, tools, and applications to automate and streamline IAM processes. Talluri, as a well-known phenomenon in the domain of cybersecurity, has led initiatives to enhance the security posture of the same environments, implementing advanced security controls and swiftly responding to security incidents. For reference, he worked on facilitating the seamless migration of IAM systems to cloud-based platforms such as Azure Active Directory and Amazon Web Services (AWS) , Google Cloud Platform (GCP), Human resource management system (HRMS), and REST APIs, ensuring compatibility and integration with existing infrastructure. Amazon Web Services (AWS) Through his work in access management, he has been able to achieve quantifiable results that actively demonstrate the efficacy of the implemented solutions. Significant strides have been made in access provisioning efficiency, with automation workflows reducing the average provisioning time from 24 hours to just 2-4 hours, marking an impressive 80% improvement. Additionally, by automating manual access management tasks, such as user onboarding and offboarding, Sampath has realized substantial cost savings. This automation effort led to a 30% reduction in IT support costs related to access management, resulting in approximately $50,000-$100,000 in annual savings. Moreover, streamlined access management processes, including the introduction of self-service access request portals, have significantly increased employee productivity. This enhancement resulted in a 50% reduction in the time employees spent on access-related inquiries, allowing them to focus more on their core job responsibilities. Moving on, the implementation of access governance frameworks and automated access reviews by Talluri and his team ensured high levels of compliance with regulatory standards, such as GDPR or SOX. Achieving a 95% compliance rate in access reviews surpassed regulatory audit requirements, mitigating the risk of fines or penalties. Lastly, proactive security measures, including the implementation of access controls and monitoring solutions, have led to a considerable decrease in security incidents related to Active Directory. Notably, incidents attributed to unauthorized access or privilege escalation decreased by 70% post-implementation. These figures emphasize the efficacy of their access management initiatives in enhancing efficiency, reducing costs, ensuring compliance, and bolstering security. The future of identity management lies in hybrid/cloud solutions that seamlessly integrate on-premises AD with cloud-based identity providers like Azure Active Directory (AAD), databases, and Jira. Organizations are likely to increasingly adopt hybrid/cloud architectures to capitalize on the scalability and flexibility of cloud services while retaining control over their on-premises infrastructure. The Zero Trust security model stands to gain prominence as organizations shift away from perimeter-based security approaches, focusing instead on principles like continuous authentication and least privilege access to mitigate data breaches and insider threats. Moreover, Identity governance and administration (IGA) is set to become a focal point for organizations aiming to enhance access governance, ensure compliance, and manage identity lifecycles effectively. Advanced authentication mechanisms, including biometrics and behavioral analytics, too, are witnessing the likelihood of being adopted to bolster security in Active Directory environments, with multi-factor authentication (MFA) becoming standard practice. Through the ability to continuously learn and adapt, it will be essential for professionals like Sampath Talluri to stay abreast of evolving trends and best practices in the maturing landscape of IAM/IGA and access management.

Sampath Talluri: Real-World Deployment of Semi-Custom ADSI Connector for Access Management

HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today! Tell us about yourself! Ritika: I am a technical product manager who is passionate about bringing compelling enterprise solutions to the market to solve customer problems at scale and deliver profitable growth. I have over ten years of experience in the field, working for large enterprises such as Splunk, Amazon Web Services (AWS), Dell Technologies, and Hewlett Packard Enterprise. Prior to working in the Enterprise technology space, I worked in the automobile industry as a manufacturing planning engineer. My Academic qualifications include an MBA and Bachelor’s degree in Industrial Engineering. Why did you choose the Technology Product Management field in the first place? Ritika: The early seeds of my career in technology product management were planted serendipitously during my first job as an industrial engineer working at one of India's largest commercial vehicle manufacturers. Focusing on the engineering implementation aspect of manufacturing new vehicles, I grew extremely curious about who decided what to build and why we were building something. It was fascinating to uncover the customer demands and business objectives behind each product line in the factory. I decided to pursue an MBA at Mays Business School, Texas A&M University, looking for formal education in the business side of things. During my MBA, I got into the product management world as an intern for enterprise storage. This role was a true 'aha' moment for me. I saw firsthand how products are conceptualized, developed, and launched. I realized that product management was where I wanted to focus my career. Over the past decade, this decision has been the most rewarding one, allowing me to lead the launches of multiple high-impact products. What tech are you most excited/passionate about right now and why? Ritika: I find data analytics and artificial intelligence (AI) applications in the precision farming field very exciting. Very soon traditional farming is going to face a lot of challenges while dealing with climate change and resource depletion. The world population is predicted to grow rapidly which puts immense pressure on agriculture to constantly increase its output. We can enrich precision farming practices such as optimized watering and controlling plantation methods based on real-time data on soil conditions, weather forecasts, and crop health by integrating AI with sophisticated data analytics closer to where the decision needs to be made. This technology has the potential and is probably our only hope to significantly increase crop yields and reduce waste by optimizing resource usage. What tech are you most worried about right now and why? Ritika: I am worried about how large language models (LLMs) like ChatGPT will continue to train on public data. Training models on copyrighted work without permission is concerning, but if laws are put in place to regulate this, AI companies will potentially run out of public data to train their models. I am concerned about how this will go down to be fair to original content creators while not impeding the evolution of LLMs. What are your hobbies and interests outside of tech? Ritika: Outside of work, I enjoy traveling, learning Spanish (currently on my 342nd-day streak on Duolingo), creating content on social media platforms, and hiking in the beautiful Pacific Northwest. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? Ritika: I have faced my fair share of challenges in the industry. There are some common microaggressions I have encountered, including being interrupted or talked over by colleagues, my achievements being attributed to my gender instead of competence, and assuming things about my interests and skills based on gender stereotypes. Any questionable misogynistic story/situation you faced/handled, and you want to share with the HackerNoon Fam? Ritika: There is one story that stands out for me. I was assigned to lead a major project involving a collaboration with a third-party company to co-brand and market a multimillion-dollar product. This was a significant opportunity, especially since I was one of the youngest product managers ever tasked with such a project. The key meeting for this project was scheduled in Europe, and I traveled there to meet with my counterpart from the third-party company. Before the official meeting, we met informally at a conference. It became immediately apparent that he was uncomfortable with me leading the project once he realized who I was. Without consulting me, he quickly included my manager in the upcoming meeting. During the meeting, he directed all questions to my manager, ignoring me despite my role as the subject matter expert. This not only put me in an awkward position but also frustrated my manager, who expected me to be the primary point of communication. After the meeting, I expressed my dissatisfaction to my manager, who was equally disappointed by the counterpart's behavior. Together, we decided that the best course of action was for my manager to decline future meeting invitations and to send a note emphasizing that all decisions and communications should be channeled through me. This incident underscores the challenges women often face in professional settings, where gender prejudices overshadow our capabilities and leadership roles. It’s crucial for organizations to support their staff by fostering an environment where such biases are addressed promptly and effectively. What was the biggest setback/failure that you faced, and how did you manage it? Ritika: Failures are opportunities to learn, working in Technology product management I live by the principle of failing fast when introducing new products and I tend to apply the same ideology in life to continuously course correct and move forward. What's your biggest achievement that you're really proud of? Ritika: I am proud of my resilience, both professionally and personally. I continue to persevere in the face of challenges and focus on my skills. I used to be proud of becoming my business school’s brand ambassador and having a billboard near IAH airport with my face on it. When my parents traveled from India for my graduation in the USA, they beamed with joy seeing my billboard on their way to the airport, which made me proud. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? Ritika: The huge gender gap cannot be attributed to one thing, but one of the biggest contributing factors is the lack of STEM awareness and education for young girls, especially in developing countries. Lack of limited role models in the workplace, preconceived notions, and biases also contribute to the problem. We all need to come together to reduce the gender gap in Tech. Some of the ways can be to Promote STEM education among young girls through programs and scholarships.
Encourage an inclusive culture that provides a safe space to support women throughout their careers.
Include more diverse leaders in tech companies. Who is your tech idol? Why? Ritika: I admire a lot of tech folks. Radia Perlman, also known as the “mother of the internet,” is pretty amazing. Do you have any advice for aspiring girls who want to join the field? Ritika: My advice to aspiring women/girls who want to join Tech is never to undersell yourself. Have confidence in your potential. I have observed women often hesitate to apply for a tech position unless they meet every single listed job requirement/qualification. But you need to remember that candidate requirements are often a “good to have” skill list for the ideal candidate, and thus not meeting every criterion should not deter you from applying. Many skills can be learned on the job, and showing a willingness to grow and adapt is highly valued in the tech industry. HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today ! this interview series with women in tech Share your story today Tell us about yourself! Ritika: I am a technical product manager who is passionate about bringing compelling enterprise solutions to the market to solve customer problems at scale and deliver profitable growth. I have over ten years of experience in the field, working for large enterprises such as Splunk, Amazon Web Services (AWS), Dell Technologies, and Hewlett Packard Enterprise. Ritika: Prior to working in the Enterprise technology space, I worked in the automobile industry as a manufacturing planning engineer. My Academic qualifications include an MBA and Bachelor’s degree in Industrial Engineering. Why did you choose the Technology Product Management field in the first place? Why did you choose the Technology Product Management field in the first place? Ritika: The early seeds of my career in technology product management were planted serendipitously during my first job as an industrial engineer working at one of India's largest commercial vehicle manufacturers. Focusing on the engineering implementation aspect of manufacturing new vehicles, I grew extremely curious about who decided what to build and why we were building something. It was fascinating to uncover the customer demands and business objectives behind each product line in the factory. Ritika: I decided to pursue an MBA at Mays Business School, Texas A&M University, looking for formal education in the business side of things. During my MBA, I got into the product management world as an intern for enterprise storage. This role was a true 'aha' moment for me. I saw firsthand how products are conceptualized, developed, and launched. I realized that product management was where I wanted to focus my career. Over the past decade, this decision has been the most rewarding one, allowing me to lead the launches of multiple high-impact products. What tech are you most excited/passionate about right now and why? Ritika: I find data analytics and artificial intelligence (AI) applications in the precision farming field very exciting. Very soon traditional farming is going to face a lot of challenges while dealing with climate change and resource depletion. The world population is predicted to grow rapidly which puts immense pressure on agriculture to constantly increase its output. We can enrich precision farming practices such as optimized watering and controlling plantation methods based on real-time data on soil conditions, weather forecasts, and crop health by integrating AI with sophisticated data analytics closer to where the decision needs to be made. This technology has the potential and is probably our only hope to significantly increase crop yields and reduce waste by optimizing resource usage. Ritika: What tech are you most worried about right now and why? What tech are you most worried about right now and why? Ritika: I am worried about how large language models (LLMs) like ChatGPT will continue to train on public data. Training models on copyrighted work without permission is concerning, but if laws are put in place to regulate this, AI companies will potentially run out of public data to train their models. I am concerned about how this will go down to be fair to original content creators while not impeding the evolution of LLMs. Ritika: What are your hobbies and interests outside of tech? Ritika: Outside of work, I enjoy traveling, learning Spanish (currently on my 342nd-day streak on Duolingo), creating content on social media platforms, and hiking in the beautiful Pacific Northwest. Ritika: Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? Ritika: I have faced my fair share of challenges in the industry. There are some common microaggressions I have encountered, including being interrupted or talked over by colleagues, my achievements being attributed to my gender instead of competence, and assuming things about my interests and skills based on gender stereotypes. Ritika: Any questionable misogynistic story/situation you faced/handled, and you want to share with the HackerNoon Fam? Any questionable misogynistic story/situation you faced/handled, and you want to share with the HackerNoon Fam? Ritika: There is one story that stands out for me. I was assigned to lead a major project involving a collaboration with a third-party company to co-brand and market a multimillion-dollar product. This was a significant opportunity, especially since I was one of the youngest product managers ever tasked with such a project. The key meeting for this project was scheduled in Europe, and I traveled there to meet with my counterpart from the third-party company. Before the official meeting, we met informally at a conference. It became immediately apparent that he was uncomfortable with me leading the project once he realized who I was. Without consulting me, he quickly included my manager in the upcoming meeting. During the meeting, he directed all questions to my manager, ignoring me despite my role as the subject matter expert. This not only put me in an awkward position but also frustrated my manager, who expected me to be the primary point of communication. After the meeting, I expressed my dissatisfaction to my manager, who was equally disappointed by the counterpart's behavior. Together, we decided that the best course of action was for my manager to decline future meeting invitations and to send a note emphasizing that all decisions and communications should be channeled through me. Ritika: This incident underscores the challenges women often face in professional settings, where gender prejudices overshadow our capabilities and leadership roles. It’s crucial for organizations to support their staff by fostering an environment where such biases are addressed promptly and effectively. What was the biggest setback/failure that you faced, and how did you manage it? What was the biggest setback/failure that you faced, and how did you manage it? Ritika: Failures are opportunities to learn, working in Technology product management I live by the principle of failing fast when introducing new products and I tend to apply the same ideology in life to continuously course correct and move forward. Ritika: What's your biggest achievement that you're really proud of? What's your biggest achievement that you're really proud of? Ritika: I am proud of my resilience, both professionally and personally. I continue to persevere in the face of challenges and focus on my skills. I used to be proud of becoming my business school’s brand ambassador and having a billboard near IAH airport with my face on it. When my parents traveled from India for my graduation in the USA, they beamed with joy seeing my billboard on their way to the airport, which made me proud. Ritika: In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? Ritika: The huge gender gap cannot be attributed to one thing, but one of the biggest contributing factors is the lack of STEM awareness and education for young girls, especially in developing countries. Lack of limited role models in the workplace, preconceived notions, and biases also contribute to the problem. Ritika: We all need to come together to reduce the gender gap in Tech. Some of the ways can be to Promote STEM education among young girls through programs and scholarships. Encourage an inclusive culture that provides a safe space to support women throughout their careers. Include more diverse leaders in tech companies. Promote STEM education among young girls through programs and scholarships. Encourage an inclusive culture that provides a safe space to support women throughout their careers. Include more diverse leaders in tech companies. Who is your tech idol? Why? Ritika: I admire a lot of tech folks. Radia Perlman, also known as the “mother of the internet,” is pretty amazing. Ritika: Do you have any advice for aspiring girls who want to join the field? Ritika: My advice to aspiring women/girls who want to join Tech is never to undersell yourself. Have confidence in your potential. I have observed women often hesitate to apply for a tech position unless they meet every single listed job requirement/qualification. But you need to remember that candidate requirements are often a “good to have” skill list for the ideal candidate, and thus not meeting every criterion should not deter you from applying. Many skills can be learned on the job, and showing a willingness to grow and adapt is highly valued in the tech industry. Ritika:

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