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Through artificial intelligence and machine learning, Hitch helps companies find the talent most compatible with their organizational culture.

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January26, 20214 min read

Hitch is the talent discovery platform that offers the information based on data and on the development of applied neuroscience with Artificial Intelligence that companies need to select the best professionals, develop leaders and discover talents, that is, find that needle in the haystack for their key positions.

Among many things that are changing, for example, the traditional job interview has changed forever. Now, video interviews are studied by algorithms and in this way it is possible to know with much greater precision and depth the qualities of the candidates. That, in addition to many other resources, are part of what Hitch offers, the tech people created by Mexican entrepreneurs.

With Hitch, recruiting tasks can be carried out remotely, having access to a number of CV's that it is impossible to manually review for any company. We also facilitate talent inclusion decision-making based on the candidate's capabilities, qualities and compatibility with the company. All of this substantially raises the level of success in hiring and long-term retention of employees.

We free up the time of Human Capital personnel in companies so that they can focus on tasks that need greater human action, such as strengthening the organizational culture and the development and training of talent.

"At Hitch , we help companies discover the talent they need to be successful," said Gabriela Ceballos, CEO of Hitch during the press conference. "This launch makes finding talent an agile, intelligent and humane experience, injecting the right amount of technology to drive data-driven insights for better decision making. The fact that everything can be done virtually makes launching this product after a year marked by the COVID19 pandemic, is good news for companies and candidates. In addition, by finding the right candidate for the right position we generate long-term happy relationships where companies and talent develop their full potential. "

This SaaS offers:

" Hitch combines the best of neuroscience and organizational psychology with technology, creating a solution that generates great results by analyzing many more candidates and screening the most suitable ones step by step to ensure that companies find who they need, in addition to generating an experience of humane, fair user and with the least possible bias, comments Dr. Ral Arrabales, PhD in Computer Science and Artificial Intelligence and VP of Product at Hitch.

Gabriela Ceballos CEO Hitch. Photo: Courtesy

Because of Hitch's potential, we were able to raise $ 400,000 in pre-seed capital. For our first year in operations we plan to have more than 100 companies in our portfolio, in addition to that we will be processing more than 50,000 jobs for our clients, assured Ceballos.

As Hitch expands its Artificial Intelligence capabilities, the company is committed to a transparency approach, providing a clear path to how algorithms are built and how success predictions are made.

Hitch has experts in technology and psychology who monitor artificial intelligence and ensure the accuracy and fairness of algorithms. For the same reason, still in the pilot phase, it has been selected as part of the program for the Prototype of Public Policy on Transparency and Explicability of AI systems led by

C Minds, Facebook, the Inter-American Development Bank Group and the National Institute for Transparency, Access to Information and Protection of Personal Data (INAI).

Hitch enables companies to make the best decisions about their talent selection from hiring to targeting the type of leadership and culture they want to create for their human capital. Our talent and culture analytics, using AI and machine learning, provide companies with a competitive advantage when recruiting through a deep understanding of their candidates and the qualities that drive success. The result is outstanding employee performance, transforming the average workforce into high-performance, exponentially growing companies.

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This Startup Uses Artificial Intelligence to Help Companies Find Employees Who Fit Their Culture - Entrepreneur

Ever wish Cher Horowitzs virtual closet existed beyond the fictional realm ofClueless? Now, it does. FINESSE is the newly launched brand using artificial intelligence to predict trends, reduce fabric waste and deliver customers perfectly coordinated outfits at just-right prices.

Before actually producing anything, FINESSE uses proprietary algorithms and machine learning technology to analyze fashion trends across the internet. We look at fashion in the same way an economist or hedge fund trader would [look at stocks], CEO Ramin Ahmari says. What are the signals that predict this stock or fashion item will be going up to record highs, and what are the chances it will underperform? From there, the brand ideates three potential drops coordinated outfits that incorporate whats trending across the industry that shoppers then vote on. (Examples of fan-favorite drops include the Maddy, an ensemble inspired by Maddy Perez of Euphoria, and the Bella, an all-white fit taking cues from Bella Hadids street style.) The pick with the most votes goes into production while the other two are scrapped, cutting down on fabric waste. The entire process takes less than 25 days, a shockingly speedy turnaround time that puts Zara and H&M to shame.

Finesse

Ahmari, who is queer and non-binary, launched FINESSE in a quest to reclaim his identity. My narrative [was] consistently taken out of my hands by labels that were forced upon me, the founder, who studied computer science and art history at Stanford, explains. Fashion was my way of regaining control over that narrative. I would wear baggy jeans and oversized hoodies to fit in with the straight guys when I wasnt ready to come out yet. Eventually, I got older and would browse the female section, allowing me to explore my femininity when I was ready to, he reflects, adding the fashion is a powerful tool for self-determination. Seeking to remedy the rigid binaries that clothing often promotes, all FINESSE drops are unisex (though the brands website mostly features female models). In addition, the companys board is comprised entirely by people of color, and over 75 percent of FINESSE employees belong to minority communities. Most of fashion today has been told from a specifically white male gaze. True equality and diversity has to start from the very root of an organization, Ahmari advocates, expounding on values that are unapologetically expressed on the labels Instagram. After the Capitol riots, the brand quickly denounced white supremacy. On the day of Joe Bidens presidential inauguration, it celebrated the end of Donald Trumps term. The companys outspokenness is refreshing, especially considering it is backed by major investors including Hoxton Ventures and Mango Capital.

Mainstream fashion has absolutely no idea about what will sell, so they play it safe and produce everything under the sun. Our focus at FINESSE is to eliminate this outrageous inefficiency.

Though FINESSE may seem like a fast fashion brand, it aims to revise the wasteful and often unethical practices the industry at large operates on. By only producing what its customers want i.e. the most voted-on drop and using 3D-modeled samples during the early stages of garment development, it reduces fabric waste and streamlines the production pipeline. Mainstream fashion has absolutely no idea about what will sell, so they play it safe and produce everything under the sun, Ahmari states. Our focus at FINESSE is to eliminate this outrageous inefficiency. We produce only what we know will sell, and we pre-estimate carefully how much demand there is based on data. In turn, the company saves money by producing such a curated range of items, allowing it to sell drops at accessible price points. Most drops, which include about three items, are sold in sets that retail at approximately $100 USD. Individual pieces average at about $30 USD.

Finesse

Getting into the nitty gritty of its production methods, Ahmari explains that the brand works with just three factories in China. We have vetted these factories thoroughly to make sure they are both ethical and invested in innovation. Our main manufacturer is particularly invested in the use of 3D renderings to improve the production process. They have seen first-hand how archaic the industry is, and are fed up by large fashion houses commandeering them to output at all costs, the CEO says. Considering FINESSEs incredibly affordable prices, its worth noting that the brand likely isnt using the highest-quality materials. The founder acknowledges the need to explore more sustainable alternatives, as well as the possibility of working with fabric mills once the company hits scale (after all, it just launched today). In addition, FINESSE has plans to push out a recycling and up-cycling initiative in the next few months. There is so much you can do with garments if youre given the right tools, Ahmari hints.

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FINESSE Is the Innovative New Brand Using Artificial Intelligence To Disrupt Fashion - HYPEBAE

New York-based Moodys Analytics has enjoyed considerable success across a number of WatersTechnologys awards programs over the yearsfor example, in 2020 it won the best credit risk solution provider category in the Waters Rankingsalthough a win in the AFTAs has always eluded the financial intelligence and analytical tools specialist. Until the 2020 AFTAs that is: This year, Moodys Analytics walks away with a pair of wins, the first of them coming in the best artificial intelligence (AI) technology initiative category, thanks to its QUIQspread offering, an AI-based financial spreading tool unveiled in 2020, designed to help institutions automate the spreading of financial statements.

Financial spreading is the manually intensive process through which lenders extract key data from unstructured financial statements from the purposes of conducting credit risk analysis on borrowers. According to Eric Grandeo, senior director, product manager at Moodys Analytics, QUIQspread uses machine learning technology to automate the financial spreading process, resulting in normalized datasets and allowing lenders to make faster and more judicious lending and credit decisions. Its a process that can be cumbersome and inconsistent, potentially resulting in costly mistakes, Grandeo explains. Lenders want to empower their relationship managers and analysts to focus more on high-value credit risk analysis tasks and increase their throughput in the most efficient way possible, and QUIQspread helps them do that.

Given the unstructured nature of financial statements, incumbent rules-based applications tend to struggle when it comes to accounting for the variety of information/data formats presented in statements. Machine learning, Grandeo explains, is the ideal technology to automate that process. Machine learning technology learns from previous practices and behaviors and can adapt to change over time without any development work, he says. Spreading is an evolving practice and needs a technology that evolves with it. Today, QUIQspread is processing thousands of spreads for customers in production who are now benefiting from significant time savings and efficiencies.

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AFTAs 2020: Best Artificial Intelligence (AI) Technology InitiativeMoody's Analytics - http://www.waterstechnology.com

Analytics has been changing the way organizations operate for a long while. Since more organizations are dominating their utilization of analytics, they are diving further into their data to build proficiency, acquire a more prominent upper hand, and lift their bottom lines significantly more.

Analytics powers your business, however, what amount of value would you say you are truly harnessing from your data?

Artificial intelligence and machine learning can help. Artificial intelligence is a collection of technologies that extract patterns and valuable insights from huge datasets, then making forecasts dependent on that data. Truth be told, AI exists today that can assist you with getting more value out of the data you as of now have, bind together that data, and make forecasts about customer behaviors based on it.

The adoption of AI has been driven not just by increased computational power and new algorithms yet additionally the growth of data now accessible. For intelligence analysts, that multiplication of data implies surefire data over-burden. Human analysts essentially cant adapt to that much information. They need assistance.

Intelligence leaders realize that AI can assist to adapt to this data downpour yet they may likewise consider what sway AI will have on their work and staff. For example, Twitter utilizes machine learning and AI to assess tweets in real-time and score them utilizing different measurements to show tweets that can possibly drive the most engagement.

Google is researching virtually every part of machine learning and is making advancements in old-style algorithms and different applications like speech translation, prediction systems, natural language processing, and search ranking.

Artificial intelligence plays a significant part in assisting organizations with handling data without forfeiting accuracy or speed.

With digital transformation widely being embraced, the volume and size of data have expanded significantly. Also, dealing with such gigantic data isnt simple. Artificial intelligence- fueled data-driven innovation can help organizations manage such data to guarantee importance, worth, security, and transparency. They can depend on AI data integration platforms to ingest, change, and use information easily and with accuracy. Such platforms give an end-to-end encrypted environment that protects information from undesirable infringing and breaches, and make them hard to work with.

Artificial intelligence and ML frameworks exist that utilize analytics data to assist you with foreseeing results and effective blueprints. Artificial intelligence- empowered frameworks can analyze information from many sources and deliver forecasts about what works and what doesnt. It can likewise deeply jump into information about your customers and offer predictions about buyer inclinations, marketing and sales channels, and product development strategies.

Artificial intelligence/ML advances empower companies across various industries to harness value from customer information with no trouble. For instance, AI data integration solutions empower all business users to map information between various fields to make it simpler to incorporate the data into a unified database. Since these arrangements can be effortlessly utilized by non-technical users, IT people need not assume full responsibility. This leaves IT to zero in on other vital tasks.

These solutions use ML algorithms to provide predictions of data, which can additionally quicken the data transformation process. Since the decisions are taken utilizing algorithms, the chance of mistakes like missing qualities, deceptions, errors, and so on, reduce. Hence, companies can use AI/ML tools to change the manner in which they deliver customer value. They can plan and integrate data and keep up data integrity, improving decision-making and boosting growth.

The advantages of AI and ML, notwithstanding, can go a long way beyond time savings. All things considered, intelligence work is a never-ending process; there is consistently another difficulty that demands attention. So saving time with AI wont decrease the staff or trim intelligence budgets. Or maybe, the more noteworthy value of AI comes from what may be named an automation dividend: the better ways experts can utilize their time after these advances reduce their workload.

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The Role of Artificial Intelligence and ML in Intelligent - Analytics Insight

During a walking tour of Queenslands Daintree rainforest in Australia, a talented guide regularly pointed out creatures that were well camouflaged into their surroundings. At one point, he directed our attention to a tree trunk, where a large grasshopper was camouflaged. The guides observations and stories wove together the connections between biology, geology and colonialism, helping explain how big and small changes could transform life in this ecosystem.

Our society has been altered by the rapid proliferation of new technologies and devices that produce digital data. Nested within and feeding on this data ecosystem, artificial intelligence (AI) executes cognitive tasks with more potency and speed than human beings. The large-scale transformative power of AI remains camouflaged in plain sight.

Through the lens of the responsible innovation in health research program at the Universit de Montral, we critically examine what lies beyond our immediate experiences of AI.

Much like driving a car, we do not need to understand how AI works in order to use its applications. And similar to ways in which the fossil fuel industry shaped the role of cars in our society, AI is delivered through powerful commercial interests and large digital and physical infrastructures. To better understand their impacts, there is an urgent need to critically appraise how AI delivers its much-touted promises.

At the onset of the Industrial Revolution, people in Montral had no clue about the kinds of infrastructures that were going to be developed to extract, exploit, distribute and use fossil fuels. Montral was ideally located to transport goods, including oil, and refineries were later concentrated along the Saint Lawrence River. Beyond negative impacts on residents health, the decisions made at the turn of the 20th century to exploit fossil fuels have had long lasting self-reinforcing effects.

And now, in the 21st century, we are seeing the changes AI brings and we need to consider the wide-ranging ramifications.

The jewel in the crown of the intangibles economy, AI needs expansive e-infrastructures that have tangible impacts and costs. Estimates suggest that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent five times the lifetime emissions of an average car.

If we choose to exploit the oil of the 21st century, we will have to build large powerful computational centres and sizable server farms. AI requires networking and cloud infrastructures to capture, analyze, share and archive vast amounts of data.

When deep learning techniques are involved, training is a key step that consists of feeding the algorithm with large and mostly unstructured datasets. The training of a single AI-based application may be split over dozens of chips and may require months to complete.

Although it only takes a low energy tap on a smartphone to use an application, its development is energy intensive and non-renewable energy sources have a much larger environmental impact.

Thankfully, data scientists are starting to calculate the energy required to develop AI tools before they are made available for use. For instance, a process involved in automating the design of a neural network through trial and error called the Neural Architecture Search (NAS) is highly energy intensive. Without NAS, training the AI tool Transformer takes 84 hours, but with NAS it takes more than 270,000 hours, thereby requiring 3,000 times the amount of energy.

Reducing the carbon footprint of AI requires a concerted effort by industry and academia to promote research of more computationally efficient algorithms and the use of more sustainable hardware and model development strategies.

Because data generated through digital interactions are worth their weight in gold, commercial agreements are likely to keep the future of AI into the hands of those with corporate interests. Exploiting data to increase corporate profits are the core business of tech giants like Amazon and Google.

Read more: It's time for a new way to regulate social media platforms

This is one of the reasons why it is important for public policy-makers to create alternative entrepreneurial pathways where data scientists and programmers who aim to design much more meaningful AI can thrive.

Could AI empower those who tackle todays major societal challenges and seek solutions for the common good? For instance, what would an eco-friendly AI tool to help us meet the United Nations Sustainable Development Goals look like? What alternative business and data governance models should be promoted for benefits to be shared equitably?

Seeing the forest and the trees could turn a more responsible vision for the 21st century into a tangible reality.

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Hidden in plain sight: The infrastructures that support artificial intelligence - The Conversation CA