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Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes,  AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. AI for Automation Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries. The following are a few of the most common: 1. AI Chatbots Chatbots powered by AI are computer programs that simulate conversations with human users via text or audio. They can be used to automate customer service, answer questions, and even make recommendations, all of which were previously done by humans. Chatbots can also be used in marketing as they can be programmed to interact with customers and provide personalized services. IBM's Watson, Amazon's Lex, Google's Dialogflow, and Microsoft's Bot Framework are some of the most well-known and widely used chatbot service providers for millions of businesses. 2. Voice Recognition AI voice recognition software is also broadly used. Customer service is perhaps one of the most common applications of AI-based automation that is already in place. However, it is also widespread in voice-enabled search and navigation, controlling home automation systems, hands-free app control, and other applications. To give you an idea, Nuance's Dragon, SoundHound's Houndify, and wit.ai are some of the most extensively used voice recognition service providers. 3. Robotic Process Automation In addition to automating manual, repetitive, and time-consuming tasks, robotic process automation (RPA) products also sped up workflows considerably. Among the examples are data entry, document processing, and customer service interactions. Some of the largest companies applying it into practice are UI Path, WorkFusion, and Automation Anywhere. 4. Image Recognition Artificial Intelligence image recognition allows computers to recognize objects, scenes, and patterns in images by analyzing and interpreting them based on AI algorithms. This technology has several applications, including object detection and classification - which are typically done by humans - and facial recognition and image search. Amazon's Rekognition, Google's Vision API, and Clarifai are among the most popular and widely used image recognition services today. 5. Autonomous Mobility Autonomous mobility refers to the use of AI to enable vehicles to operate autonomously, without the need for a human driver. This technology has the potential to transform the way people and goods are transported, resulting in increased efficiency, safety, and accessibility. AI autonomous mobility is achieved through a combination of sensors, cameras, and other detectors that allow the vehicle to perceive its surroundings. The vehicle then uses AI algorithms to process this information and make decisions about how to navigate its environment, such as adjusting its speed, changing lanes, and avoiding obstacles. One of the primary advantages of AI autonomous mobility is enhanced safety. Autonomous vehicles have the potential to significantly reduce the number of road accidents by removing human error from the equation. Besides, by optimizing routes and lowering fuel consumption, the technology can help to reduce traffic congestion and improve overall driving efficiency. Another advantage of AI autonomous mobility is increased accessibility for people who cannot drive. Autonomous vehicles, in turn, can provide a new level of independence and mobility to the elderly, disabled, or those without a driver's license. This technology has been developed by Tesla, Waymo, Easy Mile, and Boston Dynamics, to name a few. AI for Data Analysis AI is also increasingly being used in data analysis to assist organizations in making better, faster, and more data-driven decisions. AI algorithms can process massive amounts of data and identify patterns and relationships that human analysts may not notice right away. This data can then be used to inform business strategies, improve operations, and stimulate innovation. Some of the more specialized applications for these tools encompass: 1. Predictive Analytics With the help of historical data, AI algorithms can predict events in the future. Using AI, companies can predict customer behavior, sales trends, and market conditions more accurately. A number of AI algorithms are embedded in Netflix and Spotify's algorithms to help the companies better understand their users' likes and dislikes. Based on this data, services can then provide relevant recommendations to their customers to keep them engaged by listening to music or watching series on those platforms. 2. Fraud Detection Large amounts of data can be analyzed by AI to detect fraudulent activities such as credit card fraud, insurance fraud, and cyberattacks. The technology can detect unusual patterns of behavior and flag them for further investigation, improving financial transaction security and protecting organizations from potential losses. Master Card has long used AI technologies to provide a fraud score for every transaction on its network as part of its anti-fraud efforts, making the information available to its issuers. Moreover, AI is used to detect fraud in combination with Mastercard's biometrics and behavioral analysis capabilities. 3. Predictive Maintenance Data from equipment and systems can be analyzed by AI algorithms to predict when they may fail. This enables businesses to take proactive measures to avoid downtime and lower maintenance costs. Every year, General Motors spends more than $1 billion on maintenance and $7 billion on capital improvements. To reduce maintenance costs and unplanned downtime, the company now uses AI-driven tools to support the widespread adoption of predictive maintenance across its over 140 global production sites[ ]. 1 4. Supply Chain Optimization AI algorithms can analyze supply chain data to identify inefficiencies and make recommendations for improvement. There are several applications for this today: to analyze data such as sales trends, consumer behavior, and market conditions to predict future demand, allowing companies to better plan their production and inventory levels. Demand forecasting – to optimize inventory management by lowering the risks of stockouts and overstocking. Inventory management - to optimize delivery routes and reduce transportation costs by analyzing data such as traffic conditions, delivery routes, and shipping costs. Delivery optimization - to provide real-time visibility into shipment status, allowing businesses to quickly identify and resolve any issues that may arise. Supply chain visibility - to analyze data such as consumer demand, shipping costs, and market conditions in order to optimize product distribution while lowering costs and increasing margins. Blue Yonder, Aera Technology, Kinaxis, and IBM Watson Supply Chain are a few of the most recognizable companies in the supply chain optimization area. Product distribution - AI for Customer Service AI is being utilized more and more to improve customer service and provide a better customer experience. Artificial intelligence technology can assist organizations in increasing the efficiency and effectiveness of their customer service operations while also providing customers with timely and personalized support. 1. Virtual Assistants AI virtual assistants can provide personalized support to customers, assisting them in finding information or completing tasks. Virtual assistants, for example, can be integrated into websites or mobile apps to assist customers with their shopping experience. As such, Walmart customers can ask Google Assistant to add specific items to their shopping carts or learn more about their shopping habits in the app[ ]. 2 2. Sentiment Analysis AI can be used to understand customer sentiments and opinions by analyzing customer feedback such as social media posts and survey responses. This data can assist organizations in identifying areas for improvement and providing better customer service. For example, the global FMCG company Mars utilizes tools to assess video content and analyze sentiment in online ratings and reviews[ ]. 3 3. Automated Voice Response AVR tools are used to provide automated customer service over the phone and interact with customers by providing audio responses to their questions or requests. These systems can be programmed to respond to simple questions and requests, provide callers with information such as current balances, allow customers to access specific account information, and, when necessary, redirect customers to an operator or customer service representative. Alvaria, Avaya, and GeneSys are three of the most recognized companies in this field. AI for Marketing AI is being used to increase the efficiency of marketing efforts in a variety of ways. AI technology can assist organizations in better understanding their customers, boosting campaign targeting, and measuring the results of their efforts. It is used in marketing in the following ways: 1. Customer Segmentation To create targeted marketing campaigns, AI algorithms can analyze customer data such as demographic information, purchase history, and social media behavior. This enables businesses to better understand their customers and increase the effectiveness of their marketing efforts. The Amazon Personalize automated segmentation system, which matches customers with the most compatible products tailored to their personal needs, is one of the most vivid examples. This is achieved through the use of special techniques that learn how users interact with items and then segment them based on their preferences. 2. Social Media Marketing To better understand customer sentiment and preferences, AI algorithms can analyze social media data such as posts, comments, and likes. This data can be used to boost social media marketing efforts, such as by creating more relevant and engaging content. Content optimization By analyzing data to uncover patterns and trends in user behavior, this technology automates the process of personalizing content to target individual users. AI-powered content optimization tools can assist marketers in creating more personalized, compelling content to engage and convert customers by understanding user preferences. This technology also assists marketers in optimizing content for various channels such as the web, mobile browsers, apps, and others. Some of the most well-known companies in this regard are Marketo Engage, BridgtEdge, and Evergage. AI for Legal work In the legal industry, Artificial Intelligence is being used to automate repetitive tasks, improve the accuracy of legal research, and provide more efficient and cost-effective services to clients. 1. Contract Review and Analysis AI algorithms can be used to review and analyze large volumes of contracts, assisting lawyers in identifying key provisions and clauses in a timely manner. When compared to manual review, this can save time and improve the accuracy of contract review. 2. Document discovery AI algorithms can be used to categorize and prioritize legal documents, such as those used in litigation discovery. This can help lawyers identify relevant documents more quickly and make more informed decisions. 3. Legal Research Legal research tools powered by AI, such as Westlaw and LexisNexis, can provide lawyers with timely and accurate information on legal precedents, regulations, and case law. This can assist them in conducting more effective legal research and providing more informed advice to clients. 4. Document Automation Artificial intelligence algorithms can be used to automate the creation of legal documents like contracts and agreements. When compared to manual drafting, this can improve the efficiency and accuracy of document creation. Kira, Ross, Cognitiv+, LawGeex, Luminance, and Legal Robot are some of the best-known tools/companies developing and selling these technologies today. AI for Creative Activities Artificial intelligence is being used in the creation of music and video to automate tasks, generate new content, and provide artists and content creators with new creative tools. 1. Music Composition AI algorithms can generate new music by either creating original compositions or recombining existing elements in novel ways. For example, AI can analyse existing music to determine the key elements of various genres and then use this knowledge to create new pieces. 2. Video Editing AI algorithms can be used to edit video footage automatically, such as selecting the best shots, removing background noise, and color correcting. In comparison to manual editing, this can save time and improve the accuracy of video editing. 3. Content Creation AI algorithms can be used to create new types of content, such as computer-generated imagery (CGI) or motion graphics. This can provide new tools for artists and content creators to create and experiment with new forms of content. 4. Music Production AI algorithms can be used to automate music production tasks like mixing and mastering. AI algorithms, for example, can be used to optimise the balance of different audio elements in a mix, thereby improving the overall sound quality of a recording. 5. Video Enhancement AI algorithms can be used to improve video content quality by removing noise, stabilizing shaky footage, and increasing resolution. This can provide tools for content creators to produce high-quality video content with less effort. Amper Music, AIVA, Flow Machines, Synthesia, Elai, and DaVinci Resolve are among the most well-known companies in this field. AI to Identify Customer Needs Last but not least, Artificial intelligence is being used to identify customer needs by analyzing massive amounts of customer data and behavior in order to gain insights into customer preferences, motivations, and needs. One of the most visible examples of this is currently taking place in the fashion industry, with some examples provided below: This online personal styling service recommends clothing items to customers based on their personal style, body type, and preferences using AI algorithms. Stitch Fix: To determine which styles and colors are most popular, the global fashion retailer uses AI to analyze data from sales, social media, and customer feedback. The data is then used to inform their design and production decisions. Zara: This online clothing rental service employs Artificial Intelligence to recommend styles to customers based on previous rentals, preferences, and events they are attending. Rent the Runway: To inform their design and production decisions, this global fashion brand employs AI to analyze consumer behavior and preferences, such as the most popular colors, styles, and materials. Tommy Hilfiger: An Indian online fashion and lifestyle retailer employs AI to recommend products to customers based on their browsing and purchasing history, as well as their preferences and style. Myntra: Conclusion To summarise everything mentioned above, Artificial Intelligence is rapidly transforming the business world and shaping the future of many industries. AI is assisting businesses in making more informed decisions, enhancing efficiency and productivity, and providing a more personalized and effective customer experience by processing and analyzing massive amounts of data. AI is used in a variety of applications, including data analysis, marketing, customer service, and legal work. In a rapidly changing business landscape, the use of AI in these areas is aiding businesses in gaining a competitive advantage and staying ahead of the curve.
Still, despite the numerous benefits of AI, there are concerns about its potential impact on the workforce, as well as on privacy and security. As AI technology evolves, it will be crucial for companies to consider these issues and take a responsible and ethical approach to AI use. Overall, AI is profoundly and far-reaching shaping the future of business, and businesses that can effectively harness its power will be well-positioned to succeed in the years ahead. Lead image developed by stable diffusion.

AI's Growing Influence and the Future of Business

Branding is a multifaceted process that involves the creation and establishment of a unique brand identity in the minds of consumers. This identity is comprised of various elements, including brand messaging, visual identity, and experiential touchpoints that are designed to reflect the company's mission and values. In the realm of purpose-driven branding, companies seek to align their brand's purpose with the values and beliefs of their target audience, thus fostering a deeper emotional connection. According to the U.S. Census Bureau, the number of ecommerce companies in the United States has grown steadily over the past five years. In 2015, there were an estimated 1.3 million ecommerce companies in the United States. This number has grown to 1.8 million in 2020, an increase of 38%. The number of ecommerce companies is expected to continue to grow in the coming years as more consumers shift to online shopping. According to Statista, the global ecommerce market is estimated to reach . This is an increase of about 21% from 2017. In 2017, the global ecommerce market was estimated to be worth $2.3 trillion. This means that the ecommerce market has grown significantly over the past few years. $4.9 trillion by 2021 One effective means of conveying this purpose is through the utilization of active voice in brand messaging. By adopting an assertive and confident tone, companies can effectively communicate their brand's purpose and inspire their audience to take action in support of it. This can be achieved through a variety of tactics, such as crafting compelling brand stories, utilizing persuasive language, and issuing calls to action that motivate the audience to align with the brand's mission. How Branding Principles Can Be Incorporated In An Ecommerce Business: An ecommerce business should create an engaging brand story that resonates with its customer base. This story should encapsulate the company’s mission, values, and unique selling points. 1. Develop an Engaging Brand Story: Visual branding is key to capturing customer attention. An ecommerce business should focus on developing a consistent visual identity across all channels, including its website, social media, and marketing materials. 2. Utilize Visual Branding: Customer experience should be a priority for any ecommerce business. Companies should focus on creating a seamless experience for customers, from the moment they arrive on the website to the moment they receive their product. 3. Prioritize Customer Experience: Testimonials and reviews from existing customers can help build trust with potential customers. An ecommerce business should make sure to encourage customers to leave honest reviews and to feature these reviews prominently. 4. Leverage Social Proof: Quality content is key to any ecommerce business. Companies should create content that is informative and helpful to customers, and focus on providing high value content without any fluff. 5. Invest in Quality Content: As more and more customers shop online using their mobile devices, it is essential for an ecommerce business to make sure its website is optimized for mobile. Companies should focus on creating a mobile-friendly experience for customers. 6. Focus on Mobile Optimization: Branding is essential for an ecommerce business, as it helps to create a distinct identity and allows the business to stand out in the competitive online retail market. It is important to understand the branding essentials for ecommerce businesses that will help them to create a successful, recognizable brand. Defining Your Brand: The first step in creating a successful brand for an ecommerce business is to define the brand. This includes developing a brand identity, mission statement, and values. It is important to clearly define the brand's purpose and values, as this will help to create a distinct and memorable brand. Creating a Visual Identity: Once the brand is defined, the next step is to create a visual identity. This includes designing a logo, color palette, font, and other visual elements. It is important to create a memorable and recognizable visual identity that reflects the brand's values and mission. Developing a Brand Voice: It is also important to develop a brand voice that reflects the brand's personality and values. The brand voice should be consistent across all communication channels, including website content, social media posts, and emails. Creating a website: The next step is to create a website for the ecommerce business. The website should be visually appealing, user friendly, and feature the brand's visual identity. The website should also feature relevant content, such as product descriptions, customer reviews, and blog posts. Developing an SEO Strategy: It is essential for an ecommerce business to develop an SEO strategy. SEO is important for driving organic search traffic to the website, which can result in increased sales. SEO strategies should include keyword research, content optimization, link building, and more. Developing a Content Strategy: Content plays a key role in ecommerce branding. Developing a content strategy that focuses on creating interesting, valuable, and relevant content will help to engage customers and build brand recognition. Content should be distributed through various channels, such as social media, blogs, and email. Developing a Social Media Strategy: Social media is essential for an ecommerce business, as it can help to build a community of loyal customers. Developing a social media strategy that focuses on creating engaging content, interacting with customers, and responding to customer feedback will help to build brand recognition and loyalty. Analyzing & Optimizing: Finally, it is important to analyze and optimize the branding strategy regularly. This includes analyzing the website traffic, customer feedback, and other metrics to identify areas for improvement. Regular optimization will help to ensure that the branding strategy is effective and meeting the business' goals. Branding is essential for an ecommerce business, as it helps to create a distinct identity and allows the business to stand out in the competitive online retail market. Developing a brand identity, visual identity, brand voice, website, SEO and content strategies, and social media presence are all essential for creating a successful, recognizable brand. Regular analysis and optimization of the branding strategy is also important for ensuring that the strategy is effective and meeting the business' goals. Best Tools You Can Use To Build Your Ecommerce Platform From Scratch – WordPress is the most popular CMS that offers a wide range of themes and plugins to help in building an ecommerce website. 1. WordPress – Shopify is one of the most popular ecommerce platforms that offers a wide range of features and customization options. 2. Shopify – Magento is an open-source ecommerce platform that offers advanced features and customization options. 3. Magento – WooCommerce is a free plugin for WordPress that offers a wide range of features and customization options for ecommerce websites. 4. WooCommerce – OpenCart is an open-source ecommerce platform that offers a wide range of features and customization options. 5. OpenCart – PrestaShop is an open-source ecommerce platform that offers a wide range of features and customization options. 6. PrestaShop – BigCommerce is an ecommerce platform that offers a wide range of features and customization options. 7. BigCommerce – Volusion is an ecommerce platform that offers a wide range of features and customization options. 8. Volusion – Squarespace is an ecommerce platform that offers a wide range of features and customization options. 9. Squarespace – Wix is a website builder that offers a wide range of features and customization options for ecommerce websites. 10. Wix Use These AI Tools To Start Brand Online Chatbot platforms like Chatfuel, Botsify, Motion.ai, and ManyChat allow users to create and launch AI-powered chatbots for their brands. These tools can be used to automate customer service and sales inquiries, provide personalized product recommendations, and more. 1. Chatbot Platforms: AI-powered data analytics platforms like Google Analytics, IBM Watson Analytics, and Tableau allow businesses to track and analyze customer data. These tools can be used to gain insights into customer behavior and preferences, helping businesses better understand their target market. 2. AI-Powered Data Analytics Platforms: AI-powered image recognition platforms like Clarifai, Imagga, and Google Cloud Vision allow businesses to automatically identify objects, faces, and text from images. This can be used to create custom visual experiences for customers, helping them find the products they're looking for quickly and easily. 3. AI-Powered Image Recognition Platforms: AI-powered social media platforms like Hootsuite and Buffer allow businesses to automate social media posting and respond to customer inquiries in real-time. These tools can be used to manage customer engagement, boost brand visibility, and interact with customers across multiple 4. AI-Powered Social Media Platforms: Case Studies Of Ecommerce Brands Casper: One example of a successful ecommerce brand that is not commonly known is Casper, an online mattress company. Casper was founded in 2014 and has since grown to become one of the leading mattress companies in the world. Casper has successfully used ecommerce to reach a global audience and grow its business. Casper has built a successful ecommerce platform that has allowed them to quickly expand their reach and acquire new customers. They have implemented a range of strategies to drive customer engagement, such as creating a loyalty program, offering free shipping, and providing easy returns. Additionally, they have invested heavily in digital marketing to reach their target audience. Casper has also leveraged its ecommerce platform to create a unique customer experience. They have implemented a range of features such as sleep tracking, personalized sleep advice, and virtual try-on to help customers make informed decisions. Additionally, they have implemented a subscription model that allows customers to easily purchase and receive new mattresses on a regular basis. Overall, Casper has successfully used ecommerce to grow its business and become one of the leading mattress companies in the world. By leveraging its ecommerce platform, Casper has been able to reach a global audience and create a unique customer experience. This has allowed them to successfully differentiate themselves from competitors and establish a strong brand presence. Warby Parker: One example of a successful ecommerce brand that overcame the challenge of branding is Warby Parker. Warby Parker is an online eyewear retailer that has become a leader in the industry since its founding in 2010. The company was founded with the goal of making high-quality eyewear more accessible and affordable. Warby Parker quickly rose to prominence, but the company struggled to find a way to stand out from the competition. To solve this problem, Warby Parker developed a unique brand identity. The company created a fun and playful brand image, using bright colors and quirky visuals to set itself apart from other eyewear retailers. Warby Parker also created a strong social media presence, utilizing platforms like Instagram and Twitter to engage with customers and build relationships. The company also made sure to emphasize its commitment to social responsibility. Warby Parker donates a pair of glasses to someone in need for every pair purchased. This commitment to social good helped the company build a loyal customer base and establish itself as a leader in the industry. By creating a unique brand identity and emphasizing its commitment to social responsibility, Warby Parker was able to overcome the challenge of branding and become a leader in the ecommerce industry. Conclusion: Branding is an important part of any business or organization, as it helps to create a strong and recognizable identity that can be used to attract customers and build relationships. A strong brand identity helps to differentiate a business from its competitors, as well as to create an emotional connection with customers. Branding also helps to create a unified image across all marketing channels, and can help to create a consistent customer experience. One of the most important aspects of branding is developing a unique and recognizable logo. This logo will be used in all of the company’s marketing materials, from business cards to website designs. A logo should be simple and memorable, and should be easily recognizable by customers. It should also be versatile, so that it can be used in a variety of contexts. Another important aspect of branding is developing a strong tagline or slogan. This should be concise and memorable, and should accurately reflect the company’s values and mission. It should also be easy to remember and should be used consistently across all marketing channels. Creating a website is also an important part of branding. A website should be designed to be attractive and easy to navigate, and should include information about the company, its products and services, and its mission. The website should also include contact information and links to social media accounts. There are a number of free tools available to help businesses and organizations create and maintain a strong brand identity. Canva is a free graphic design platform that can be used to create logos, website designs, and other visuals. Hootsuite is a free social media management platform that can be used to schedule posts and monitor conversations on social media. Mailchimp is a free email marketing platform that can be used to create and send email campaigns. Branding is an important part of any business or organization, and can help to create a strong and recognizable identity that can be used to attract customers and build relationships. By using free tools such as Canva, Hootsuite, and Mailchimp, businesses and organizations can create and maintain a strong brand identity that will help them to stand out from their competitors.

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Five benefits of attending hackathons My story Hey, folks. I have experience participating in hackathons and being a mentor at such events. Back in 2019, I attended by in London. This was an amazing event with more than 100 attendees. With this article, I wanted to share my experience, talk about my insights, and hopefully motivate you to attend hackathons in the future. SmallBizHack Intuit 1. You improve your soft skills, quickly If I’m honest, the main challenge of this hackathon for me was not related to engineering: it was about building a team. This required a lot of communication with many people at the event while trying to build relationships and trust quickly. I managed to organize a team of 4 people. With each team member being from a different country (Kazakhstan, Ukraine, India, Turkey), I had a chance to improve my intercultural communication skills on top of learning more about managing a team in an intense and fast-moving environment of a hackathon. https://twitter.com/sasha_khivrych/status/1099703955842629634?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1099703955842629634|twgr^1f9d6d8d813cba3b2858e33a71d7c348523fa662|twcon^s1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Fquery%3Dhttps3A2F2Ftwitter.com2Fsasha_khivrych2Fstatus2F1099703955842629634widget%3DTweet?embedable=true It was a great opportunity for me to become better at listening to others, giving feedback, and accepting criticism. I feel like this experience made me a better leader. 2. You learn to brainstorm and make better product decisions I did not have a clear idea of what I wanted to build during the hackathon. A day before the event, I started brainstorming and decided it should be something for the hiring process. I then spent the first day of the hackathon refining product ideas with my team, starting with the problem definition and finishing with potential impact and benefits for our future clients. The final product vision was to create a platform that would automate and improve the hiring experience for companies and candidates. With a single platform, candidates would go through the entire interview process and eventually even be able to accept their job offer via our platform. From the company's side, they can build out interview stages with initial screen calls, quizzes, tasks, and scheduling face-to-face interviews. Moreover, this platform would contain a lot of templates for building interview stages and automation processes to analyze CVs, collect candidate progress and build leaderboards with candidates with the best results. As you can see, we came up with an interesting product to solve important hiring problems. A hackathon can teach you a lot about the product development process, just within the space of a few days. 3. You get exposed to new technologies One of the event conditions was to use QuickBooks API and Clarifai API or Vonage API (formerly, Nexmo) to win partner nomination. We were using all three APIs in our MVP. For example, we were using Nexmo to perform phone and video calls for interviews. One of the exciting features was to perform an initial screening phone call using Nexmo API and have a bot ask all the screening questions. Then the recording with answers was sent to Clarify API to analyze the interview result. It is quite a new experience to be interviewed by a bot, but we will probably see something similar in the future. Moreover, our platform used Quickbooks API as an accounting solution. I had not used any of these three technologies before the event, so I enjoyed exploring them and learnt quite a lot! Representatives from each company were there to support us and help us master the APIs quickly, which was another benefit of the hackathon. A hackathon can be a great excuse to play with cool and exciting tech that you aren’t exposed to in your everyday work life. 4. You meet new people in the industry Like with all public events, a hackathon is a great way to expand your professional network. I met many professionals from different business areas. It was exciting to speak with CEOs, software engineers, and designers and learn from them. I cherish the relationships I built during the hackathon and look forward to expanding my network even further during future events. 5. You (might) win I’m not going to lie: it feels nice to win! I was so happy when my team became the winning team of the Nexmo (now called Vonage). https://twitter.com/IntuitDev/status/1099728634896703490?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1099728634896703490|twgr^8ca4b2f881a41bdf1e7fd983e10eb0796f1e0ddf|twcon^s1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Fquery%3Dhttps3A2F2Ftwitter.com2FIntuitDev2Fstatus2F1099728634896703490widget%3DTweet?embedable=true Winning at this hackathon was an important milestone for me professionally. I became even more motivated to improve my software engineering and leadership skills and keep working towards my dreams. By the way, you can also expect to win amazing prizes at most hackathons (money, tech gadgets, etc.). Moreover, I won a gadget just by taking part in a survey. There are plenty of opportunities to win prizes and cool swag at a hackathon. 😉 https://twitter.com/IntuitDev/status/1099726219338223617?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1099726219338223617|twgr^2b784b62e4dd0a142337b1a2ce7b83ba054e0f88|twcon^s1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Fquery%3Dhttps3A2F2Ftwitter.com2FIntuitDev2Fstatus2F1099726219338223617widget%3DTweet?embedable=true Conclusion Learning from my experience, you can see that attending a hackathon is a powerful way to improve professional skills, build your career and expand your network. If you have never participated in a hackathon, I hope this article convinces you to try it. Originally published . here

You Should Participate in Hackathons; and Here's Why

Face recognition is a method of biometric identification through an image, video, or other audiovisual element of an individual’s face. Over the last decade, the global face recognition technology market has grown at an unprecedented rate. The needs caused by the pandemic also changed the dynamics in face recognition technology and accelerated the invention of new tools and solutions. Today, face recognition software is widely used in mobile banking, telemedicine, airports, hotels, and digital marketing. In this article we’re going to look at the biggest market players and provide a list of the best facial recognition software solutions so that you can choose the one that meets  your business goals. Best Face Recognition Software Providers: Our Top 9 1. FaceFirst FaceFirst is a facial recognition software platform designed to be scalable, fast, and accurate while maintaining the highest levels of security and privacy. FaceFirst provides surveillance, access control, mobile face recognition, and biometric data. The key features of FaceFirst include: Emotion recognition Age recognition Voice recognition Behaviour analysis Gender recognition 2. SenseTime This solution establishes a list of authorized drivers to facilitate enterprises’ automated management of employees and customers alike, lowering operational risk. Going beyond facial recognition, it also provides a customized driving experience through automatic seat adjustment, air conditioning set up, and multimedia system activation. SenseTime key features: 2D and 3D liveness detection Face matching and facial effects Gesture recognition Background segmentation 3. CompreFace CompreFace is a new and promising face recognition solution that can be easily integrated into any system without prior machine learning skills. It can be used to track the productivity of employees, control access to sensitive data and secure environments, and immediately identify visitors to give them top-notch customer service. CompreFace has made our list of best facial recognition software solutions for several reasons: It is open source and totally free The service can be set up and used without being an ML expert CompreFace uses one of the most popular face recognition methods with high accuracy The solution features a UI panel with roles for access control It starts quickly with one docker command 4. Trueface Trueface is a face recognition company that applies advanced computer vision technology to camera footage and images to enable businesses to make immediate decisions based on identified patterns. Trueface has developed a suite of SDKs and a dockerized container solution that harness the powers of machine learning and artificial intelligence to transform your camera data into actionable intelligence. Trueface key features: Face extraction and landmark detection Head pose estimation High precision modules for maximum accuracy Ability to operate completely offline 5. Clarifai Clarifai is a leader in artificial intelligence solutions, helping businesses and developers solve real-world problems with machine learning tools. It offers two powerful ways to integrate AI — Clarifai API and the portal. The face detection system returns probability scores based on the likelihood that the image contains human faces and coordinates locations of where those faces appear with a bounding box. Clarifai core features: Ethnicity recognition Gender recognition Layered security 6. Amazon Rekognition Amazon Rekognition makes it easy to add image and video analysis to your applications. You just provide an image or video to the Rekognition API, and the service can identify objects, people, text, scenes, and activities. Amazon Rekognition also provides highly accurate facial analysis and facial recognition. You can detect, analyze, and compare faces for a wide variety of use cases, including user verification, cataloging, people counting, and public safety. With Amazon Rekognition you can get information about: Facial landmarks, such as the position of eyes Detected emotions Age and gender Pose 7. Kairos Kairos has launched the Kairos Video Analytics Camera and Dashboard to help businesses get more customer data insights through customer identity and emotion detection. The Kairos Camera divides live videos into individual images and leverages facial recognition technology to collect customer identities. To ensure companies deliver a personalized experience to their customers, the device collects data about: Age and gender Time and customer status Emotions 8. Face X FaceX provides a platform for firms to easily implement Facial Recognition into their applications. Its versatility enables developers to integrate high accuracy face recognition APIs and SDKs with only a few lines of code. The main advantages of Face X are: High accuracy and better gender profiling Spoof detection Face tracking (useful if you need to monitor and track movements after detection) No racial discrimination Low cost 9. Paravision Paravision is the world's most advanced face recognition platforms, trained on an ever-expanding private global dataset of over 12 billion photos and videos. The software provides a comprehensive toolset for developing advanced face recognition solutions including face detection, face verification, face identification, and real-time streaming video. Paravision’s key features include: Face clustering Spoof detection Age estimation and gender detection Phenotype detection Emotion detection Behavior recognition capabilities What is the best face recognition software available on the market today? Making a strategic decision about what face recognition software to choose is not an easy thing. Each one includes unique features that suit specific business needs. But regardless of whether you’re a small start-up or a large-scale enterprise, face recognition technology can be the next step in improving your business efficiency.

Best Facial Recognition Software to Use in 2021

The term ‘curation’ is commonly associated with museums or libraries, not data science. However, much like the work that’s done on rare paintings or books, data curation tools make the most important data easily accessible to engineers as they build complex machine learning models. Without curation, data is difficult to find, analyze, and interpret. Data curation tools provide meaningful insights and enduring access to all your data in one place. In this article, we’ll dive into the importance of data curation for computer vision specifically, as well as review the top data curation tools on the market today. What is data curation? Data curation is the act of organizing, enhancing, and preserving data for future use. In machine learning, data curation describes the management of data throughout its lifecycle: from its collection and initially storage, to the time it is archived for future re-use. This process is all the more important for computer vision engineers, who deal with massive amounts of visual data on a daily basis. Instead of using manual methods such as writing ETL jobs to extract insights, data curation tools provide a streamlined way to access the right data whenever you need to. The importance of data curation for machine learning Under the hood, data curation tools directly influence computer vision model performance. Using data curation tools, engineers can get a better understanding of the data they’ve collected, identify the most important subsets and edge cases, and curate custom training datasets to feed back into their models. The best data curation tools enable you to: Make it easy to obtain insights on key metrics, as well as the general distribution and diversity of your datasets regardless of sensor type and format. Visualize large scale data: Quickly search, filter, and sort through the entire data lake by making all features queryable and easily accessible. Enable data discovery and retrieval: Identify the most interesting segments within your dataset, and manipulate them within the tool to create completely customized training sets. Curate diverse scenarios: The tool should fit well within your existing workflows and toolset. Seamlessly integrate: What are the best data curation tools for computer vision? With an overwhelming amount of AI products and platforms popping up year after year, how do you know which will provide the most value? Read on below to find out which data curation tool is the best fit for your computer vision project. 1. Aquarium Learning Aquarium is a data management platform that aims to make it easy to identify labeling errors and model failures. With Aquarium, users can version and combine model predictions with their ground truth. Aquarium is especially focused on curating and maintaining training datasets, catering less to raw data management use cases. This is because data exploration in Aquarium is predominantly tied to model predictions and ground truth labels. Users can access Aquarium via their cloud platform or API. However, they currently do not offer on-premise or VPC deployments, and there are no external integrations. - Aquarium supports image, 3D, audio, and text data. They also support multiple annotation types, such as classification, detection, and segmentation. Wide range of use cases - Users can manipulate evaluation thresholds and obtain interactive visualizations to obtain required samples quickly. Interactive model evaluation - Users can collaborate with each other on the Aquarium platform to build data subsets, associate them with issues, and identify new data for annotation. Collaborative features 2. FiftyOne Developed by Voxel51, FiftyOne is an open-source tool to visualize and interpret computer vision datasets. The tool is made up of three components: the Python library, the web app (GUI), and the Brain. FiftyOne does not contain any auto-tagging capabilities, and therefore works best with datasets that have previously been annotated. Furthermore, the tool only supports image and video data, and does not work for multimodal sensor datasets at this time. Unlike other tools, FiftyOne is designed to be used by individual developers rather than teams, functioning like a programming IDE. Today, the platform lacks collaborative features; for example, a single instance cannot host multiple user accounts. FiftyOne taps into TF and Pytorch dataset zoos to provide access to a variety of open datasets and open-source models. Model & dataset zoo - - Via the Brain, a separate closed-source Python package, users can quantitatively assess the uniqueness, mistakenness, and hardness of data. Advanced data analysis - FiftyOne directly integrates with popular annotation tools such as . They also have tight integrations with Jupyter and Colab Notebooks making it easy for users to run FiftyOne through Python notebooks. External integrations LabelBox , 3. Scale Nucleus Launched in late 2020 by , Nucleus is one of the newest data curation tools to hit the market. The Nucleus platform allows users to collaboratively search through image data for model failures. As of now, Nucleus only supports image data, with no support for 3D sensor fusion, video, or text data. Scale Users can access Nucleus via their cloud platform, API or Python SDK. Currently, Nucleus does not support on-premise deployability. - Users can search for visually similar images based on one or multiple base samples and associate custom tags with them. Visual similarity - Using the Nucleus SDK, users can create flexible metadata schemas. Nucleus provides smart methods to detect and create schemas using the annotation format provided. Metadata schemas - Users can create model entities and associate corresponding runs with them. Hence, models can be versioned based on runs (dataset & predictions). Model versioning 4. Clarifai Clarifai is an end-to-end solution for labeling, searching & modeling unstructured data. One of the first AI startups, they provide a platform for modeling image, video, and text data. While Clarifai’s original focus was enabling users to build custom models, they’ve recently added several data curation features including auto-tagging, visual search, and annotations. Ultimately, Clarifai is more of a modelling platform and less of a developer tool. They are best suited for relatively inexperienced teams getting started with ML use cases. - Clarifai offers a broad library of pre-built AI models, including anything from food to facial recognition. Ready-to-use model gallery - The platform supports for image, video, and text data. Wide range of data types - With the platform, users can customize or retrain existing models or create new ones from scratch. Model customization - In addition to their modelling platform, Clarifai offers fully managed annotation services through their Scribe LabelForce data labeling service. Data annotation 5. SiaSearch is a data management platform for computer vision data. Consisting of a scalable metadata catalog and query engine, SiaSearch enables developers to easily search through visual data, add metadata to frames and sequences, as well as assemble custom subsets of data for training or testing. SiaSearch With deep roots in autonomous driving, the SiaSearch platform is used by many OEMs, Tier 1s and tech companies. Aside from autonomous driving, SiaSearch also has solutions for robotics, retail, and more. - One of the only tools that can support 3D sensor fusion data, SiaSearch can analyze large volumes of unstructured sensor data, providing insights at the frame and sequence level. Specialized in sensor data - SiaSearch employs a large catalog of pre-trained extractors to automatically add frame-level, contextual metadata to raw data. Additionally, SiaSearch provides a toolbox for quick extractor development, allowing developers to integrate their own extractors. Auto-tagging capabilities - The SiaSearch platform features a unique, proprietary architecture that combines numeric and sequence-based queries to enable noticeably faster performance. Fast performance - Users can access SiaSearch via their web-based GUI or programmatic API. SiaSearch also supports cloud or on-premise deployment for enterprise users. Flexible workflows & integrations Interested in data curation? The right data curation tool can dramatically reduce the time spent on manual processes, allowing engineers to focus on what really matters - building great models. Lead image via Tobias Fischer on Unsplash Originally published by Clemens Viernickel on: https://www.siasearch.io/blog/best-data-curation-tools-for-computer-vision and has been reposted with permission.

5 Best Data Curation Tools for Computer Vision in 2021

Lessons learnt from going the full mile. Pet to Prod — Illustration by yours truly As someone who loves experimenting with new ideas and , I find myself working on side projects quite often. Sometimes for a day or a week, then I quickly lose interest and move on to the next burning desire. The projects don’t really get abandoned, rather, I convince myself that the necessary skills have been learnt…but this is not true! tools A few days ago, I was working on that automatically tags a picture using classification engines powered by . After successfully setting up the handlers, user interface, and confirming that everything works on my local machine, the desire to call it done and move on to something else rose again…but not this time, this one was going all the way to production. this simple Go project Clarifai API How do you hone good skills if you continuously practice bad form? A simple Go app, a few hundred lines of code, with only one job…how hard could it be? Well, quite hard. There is a myriad of things developers hand-wave with the excuse. You’ve probably seen a bunch of tutorials where API endpoints and secrets are hardcoded into the program. How do you hone good skills if you continuously practice bad form? So, I was going to do things the right way and take this project the full mile. Ended up learning a couple of lessons. “you would not do this in production, but this is just an example” Lessons Learnt tagifai.herokuapp.com Tagifai | What's in your photo? Automated photo tagging using Clarifai’s Image Classification Neural Networks. . Writing an article about what you did is kinda good. Perhaps with videos showing how it works. But what should be the crowning moment of your efforts is actually getting it in the hands of real users! Side projects often solve a user problem, so if your ideal users cannot engage with your side project, you failed. And no, a Github repo is not enough because the regular Jane will rather not clone a repo — even if she is a developer. Demo or it didn’t happen Assume the whole internet (well, maybe a few friends) will be checking out your work. The least you can do is a detailed README outlining the purpose of the project and how to get it running. Being a small project is not an excuse, that should actually make the documentation easy. Clear documentation is not optional, it is mandatory. How do you know your magic port on your local machine will be free in production? You don’t. Environments like Heroku randomly assign ports to your process (by default), is your program written to account for this? Basically, there are multiple factors that can make your perfectly running local application a brick in production, and there’s only one way to find out. It is not deployable code until it is running in production. Compared to platforms like , is still an infant. Node has established tools, documentation, and tutorials on how to do almost anything on platforms like Heroku. I found the lack of updated guides on deploying a Go app to Heroku quite frustrating. You don’t see this part if you never try to deploy your app. You face the karma of platform immaturity. Node.js Go dep-based It is one thing to write code and another thing to deploy it. There are conventions, best-practices, and tools (heck even git commands), that you might be totally oblivious to until you try to send you program to production. Some organizations have a DevOps team (or that one person who knows all about production :), so “mortal” developers are far removed from the realities of deployment like managing SSL certificates. It reveals gaping holes in your knowledge. Action Items Clearly, going the full mile with a side project is demanding. So, to improve my chances of success, I will be tackling future projects with these guidelines. Only work on projects that have a well-defined user. In other words, they do only one job well. Spend a day scoping the entire project based on your current experience with the required tools. If you can’t ship a (MVP) to production in 2 days, you should probably pick something smaller. Minimum Viable Product Develop code with the awareness that this is a bonafide production application. That means good documentation, proper security measures, and defensive programming. Evaluate the success of side projects by getting a few target users to try it out. This could be your buddies at work or followers on Twitter. Keep every side project running and functional. Avoid having a Github repo that “used to work” 2 years ago (guilty of this myself) — if you won’t support it anymore, then it is best to take it down. And that’s it! If you enjoyed this article, please give me some claps so more people can improve their side projects. Perhaps you have some other lessons to share, do leave a comment. Thanks!

Ever shipped a pet project to production?

From simple text-based services to chatbots, we’re spending more and more time on messaging apps. Beyond 1:1 and group chat, messaging apps are growing more innovative, more powerful, and have become a linchpin for customers, businesses, and teams across every industry and vertical. As a result, we’ve seen a flood of new messaging applications of all shapes and sizes entering the market. In fact, in monthly users, and the gap continues to grow. More and more functionality is taking place directly in the chat interface outside of sent and received messages. messaging apps have surpassed social networks So how do you set yours apart from the rest? To do so, you need a certain set of core, established features that users have come to expect. Without those, your app is doesn’t stand a chance. But you want to take your’s to the next level, differentiating it from the thousands of chat apps out there, or build messaging into your existing application to deliver a better user experience and drive engagement. In using the vast number of incredibly accessible and affordable APIs, SDKs, integrations, and infrastructures-as-a-service, you can do just that. In this post, we’ll start with features every user expects, then move into innovative ideas for building chat of the future. Expected Features of Any Chat App These are the fundamental features that every chat has (or should have) today: Send and receive messages in realtime, either one-to-one, or one-to many. Join and leave a chatroom. Messages should be stored and easily retrieved, either through a option or infinite scroll. Load more messages Messages should be time stamped. A user list (eg. buddy list) that updates in realtime as users go online/away/offline. Typing indicators to show when someone is actively typing. Alerts for offline users via push notification when they receive a message. Only authorized users to have access to channels to keep the conversations private when necessary. Ability to invite other users to a group or kick out/block them from a group for inappropriate actions. .gifs. So many .gifs. Emojis 😀 Chat Features to Set Yourself Apart Obviously, we’re all very familiar with the features listed avobe. Give or take, they’re apart of basically every messaging application out there today. So let’s get into why you’re really here, a list of messaging features that set your messaging app apart. These kinds of features are redefining the way communication takes place and continues to push the space forward. Programmability Before getting into specific differentiators, it’s important to first discuss how important it is to build your chat application with programmability in mind. What exactly is ? programmability Programmability means your chat app is “event-driven”, which gives you complete control over what happens in between a message being sent and a message being received. This could be adding business logic, alerts, or triggering 3rd party APIs. This design drives to innovative features listed below. Geolocation Instead of forcing users to switch between messaging and mapping apps, deliver geolocation directly to chat users. Cotopaxi delivers geolocation delivery updates directly in chat. Cotopaxi delivers geolocation delivery updates directly in chat. Embed live maps with realtime geolocation tracking directly into a message stream. eCommerce and on-demand companies can utilize this to allow customers to monitor their delivery or service in realtime. Integration examples: Mapbox, Esri, Google Maps Initiate directions and ETAs and publish them directly to the message stream. Combine with text-to-speech to get audio directions as well. Integration examples: Mapbox, Esri eCommerce and On-demand Run an entire shopping experience, from browsing to fulfilling orders with chat. Cotopaxi sends order updates via chat. Cotopaxi sends order updates via chat. Utilize chatbots with automated and AI-powered cognitive capabilities to assist shoppers with browsing, ordering, and troubleshooting orders from a messaging interface (more on chatbots later in the post). Stream order updates and alerts, like push notifications and SMS, as the order moves through the fulfillment lifecycle. Clicksend Integration examples: (SMS), Infobip (SMS), RingCentral (SMS), SendGrid (Transactional Email) Moderation There’s a lot of trolls out there. Chat apps today need moderation tools in them to filter messages on the fly, not only for text, but also for images. Moderate content flowing through a chat as the messages are published on the fly. This could filter out inappropriate messages, block trolls, or even remove competition mentions (for live events and launches). Integration examples: SiftNinja, Neutrino Moderate images on the fly using image recognition to filter out provocative images. Integration examples: Clarifai, Sightengine, AICeption Live Events Chat can compliment a video or live event, and allow users to interact with one another. Combine a chat application with a live streaming video, and showing how many users are currently viewing the live event. Collect and aggregate geolocation data of users to gain insight on how many people around the world are watching the event. Allow users to vote on custom polls to increase user engagement. Add interactive features like allowing users to raise their hands or upvote other messages. User Experience Build delightful user experiences for users. Send larger files of any format in realtime. Rather than sending the entire file, efficient chat applications send a reference to where the file is hosted for the end user to download. One should be able to bookmark/star important messages and see them later on. Message search and message tagging/pinning. Automated and AI-powered Chatbots Chatbots are difficult, but luckily for you, there are a ton of powerful chatbot services on the market today. You should be able to build your chat microservices to power your chatbots, and integrate 3rd party chatbot services directly into your application. Poncho delivers weather updates via chatbot in a chat app. Poncho delivers weather updates via chatbot in a chat app. Use automation to send responses when a situation is predictable. For example, a user starts chatting with a support agent on a website. While an agent may not be available immediately, you can collect user’s information like email as automated response saying, “Our agents will reply back in a moment. Please provide your email”. This benefits user as they won’t have to wait for long to hear back from agent and frees up time for agents. Use automation to send responses when the data is available and accessible. In the example of an ecommerce website, the user can type in a chatbox, “I would like to know the status of refund for my order”. The intent of this sentence is “refund status”. An automated response could be sent to the customer in with a mini form like “Please provide order ID and your email”. This enhances the user data collection experience. We can make users’ experiences better by asking questions and giving automatic responses contextually depending on where the user is on the site. While they are on the payment page, you can show them a personalized coupon that they can use. Combine activity history with customer inputs to make recommendations and answer questions in realtime. Integrations and APIs This is similar to chatbots, but more broadly speaking. However, the technology is related, in that you should be able to build your own chat microservices to integrate 3rd party APIs. Interact with 3rd party apps and APIs using commands (like ) to make your tasks more efficient. Slack apps Utilize 3rd party alerts and notifications services to trigger SMS, mobile push notifications, and browser notifications. Initiate a video call or interactive collaborative environment with services like Cisco Spark. Create your own API which can be called from chat using a command that fulfills your needs only. Analytics You should give your business users full transparency into the usage of their application. For customer support types of messaging apps, allow business users to see average chat time, connected end users, open and closed chats, or even sentiment ratings of conversations (using APIs like Watson Natural Language Processing). Monitor geolocation of connected users to gain a better understanding of where your users are located. Looking Forward We’re seeing chat move from simple messaging to widespread adoption across every vertical, from healthcare, to eCommerce, to live events. It has become a linchpin of human communication. In our next post, . we’ll show you how to build these features yourself, using our brand new ChatEngine Download eBook here! If you’re looking for a in-depth look into chat, look no further than our new eBook: In it, we cover: Chat is More Than Hot Air — How to Build the Digital Future . the different flavors of chat, choosing the right technology from what’s available on the market today and a comparison of build vs. buy and hosted vs. open source. The current landscape of chat technology: a view into how chat apps are changing from simple messaging to transform how industries and users communicate. How chat is evolving: a look at what features and experiences can be delivered to set yourself apart from the rest. How to ensure you’re at the forefront of innovation: Originally published at www.pubnub.com .

Building Chat of the Future: What You Need, and What You Really Need

A brief infographic at the end of this blog. AI — Artificial Intelligence is to mimic human intelligence for better life. The invention of that learn and perform actions took place in the classic times. Alongside , there were many fictional stories and dramas depicting artificial things and their immense potentials. artificial things by Calculus Ratiocinator Llull Marry Shelly’s Frankenstein? You must watch it if you haven’t. The idea behind machines being able to imitate humans, and their cognitive process like , , and finding out is, analysing sorting patterns — which means machines can simulate any process of formal reasoning (from ). Church-Turing thesis Wiki In common terms, machines can imitate anything with connected and meaningful information (datasets). Theory that backed up the brains of creators like and . Allen Newell, Herbert Simon, John McCarthy, , Marvin Minsky Arthur Samuel PDAbM — Public Display of Attention by Machine ;) When machines can’t watch someone misspelling their creator’s name, Well, these are some of the bold stories from our past around (Artificial Intelligence). If we look into AI’s present state and what we are up to — is prevailing from that are online to we have in our AI AI (Artificial Intelligence) bots things home. The best part is, we now have of helping us and r. cavalcades AI machines stay connected, informed, only do things that matte For the later ones, and objects that barely need our attention come hand in hand. automated tools Imagine the days when speech recognition will have transformed into ‘ ’. recognizing intents from eyeballs’ dilation range Or, one that is called which stores the behavioural pattern of every particle of a subject. Which on requirement lets the subject travel through the encrypted particle patterns and produces a brand new outcome. Dr. Quantum Do you still think ? that it lacks a unit of time? Here is the . time never stops Chronon Let’s quickly jump into the that are making our day-day life darn effortless! list of 50 AI (Artificial Intelligence) tools AI “This as a hitchhiker is so smart which is driving to our desired destination having our least conscious of what’s happening” Artificial Intelligence for Vision — helps you to organise media libraries Clarifai — visual analysis for medical and transport industry Cortica — image detection for a range of uses and industries Deepomatic — brand and face recognition DeepVision — makes satellite imagery useful Descartes Labs — the first intelligent cloud video platform Flixsense — computer vision for automotive and human detection FotoNation — matches similar products and helps to suggests combinations GrokStyle — facial recognition Haystack — uses facial recognition to help you decided on job candidates HireVue Artificial Intelligence for IoT/IIoT learns about shoppers behaviour in your store Black — helps to unify application development and data science C3 IoT — sensor analytics solution for businesses KONUX — machine learning for manufacturing process optimisation Imubit — a range of workflow optimisation products for fuel and industry Maana — helps you develop, deploy, and operate industrial apps Predix — helps renewable energy companies utilise their data better Planet OS — manufacturing analytics Sight Machine — automates data engineering for data science Sentenai — add a voice assistant to your connected product Snips — Artificial Intelligence for Development develop more adaptive, trusted and programmable AI models Bonsai — helps predict project timelines Deckard.ai — adds intelligent decision making to web and mobile apps Fuzzy.ai — connecting projects with the right team Gigster — augments your coding environment with web available knowledge Kite — deep learning platform for prediction and personalisation Layer 6 AI — makes developing chatbots for your business easy Morph — make your bot smarter, by helping it self learn Ozz — rapidly web and mobile app testing RainforestQA — increase server uptime and predict downtime SignifAI — Artificial Intelligence for Data Analytics — helps with sentiment analysis, search relevance, and more CrowdFlower — data science platform for prototype, deploy, and run at scale Dataiku — enterprise data science platform for R&D and production DataScience — platform for collaborating, building and deploying Domino Data Lab — makes DS accessible to analysts with Open Source algorithms Exploratory — helps you learn, work, and play with machine learning models Kaggle — makes data science teams more productive RapidMiner — helps DS teams put machine learning models into production Seldon — a platform to build, test, and deploy AI algorithms SherlockML — research engine, capable of discovering complex patterns in data Spark Artificial Intelligence for BI (Business Intelligence) LOL! behavioural AI for IoT Arimo — a suite of intelligent applications for enterprise Ayasdi — a range of products to improve enterprise products DataRobot — discovers events and breaking information before the news Dataminr — helps you to answer questions about your business Electra by Lore — a smarter Salesforce Einstein — adds intelligent decision making to web and mobile apps Fuzzy AI — helps you index, search, visualise and analyse your data Logz.io — is a framework for temporal pattern recognition and prediction NXT AI — to transform raw data into useful information automatically Paxata — Seems like a lot? There are many other routine AI technologies that give us precise and actionable data. The better the data are used, the better our future will be. If you come across any noteworthy AI technology, add them in the comments. As I promised, here is the infographic of List Of 50 Unique AI Technologies What else! Leave some 👏👏👏 :)

List Of 50 Unique/Routine AI Technologies

Every startup founder knows hiring talented developers is one of the biggest challenges facing fast-growing companies. For every enticement you can offer — competitive salary, generous benefits, a rewarding role — dozens of other startups will line up with similar amenities. Not to mention, you’re competing with deep-pocketed giants like Google and Facebook. But you have a secret weapon: your engineering brand. To help give you an edge in defining a compelling engineering brand, we tapped some of the world’s most prominent tech evangelists for advice. Tech evangelists work to cultivate deep relationships with developers, and arguably understand better than anyone how to attract and retain talented team members. Here’s what they had to say: Encourage developers to have a voice online and offline Developers are aware you don’t stay in a company for life any longer. On the contrary, the average retention is less than two years. That’s why it’s important to allow developers to take part in activities outside your company, says Microsoft Developer Evangelist . Allowing your talent to release work as open source, contribute to OSS products your company uses, write about their work and attend conferences to talk about themselves is incredibly motivating. This approach allows developers to keep up with new technologies and stay relevant, Christian says, and shows you aren’t afraid to give your technical talent a voice. If and when people do leave your company, they’ll take their outside work with them — and your name will be attached as the company that empowered them to create it. Christian Heilmann Invest heavily in high-quality documentation Your docs are your brand, says Developer Advocacy Lead from Slack. Well-written, clear, useful documentation communicates that the API and the product underneath are equally well thought out and architected. Bear recommends every company start the documentation process by crisply defining the target audience. Who are they? What can you assume they know about your product and stack? Then write for that group. It’s a simple strategy, she says, but great docs are the foundation for great developer brands like Stripe and Twilio. Bear Douglas Define a three-way value proposition for developers, API providers and users What are the best outcomes for the API provider, the end-user, and the developer integrating the APIs? Adobe Lead Tech Evangelist recommends figuring out what makes your product desirable for all three parties, and working backward from there. If the value prop for any of these parties — business partner, customer or API provider — is missing, you’re in trouble. Ash Ryan Arnwine Once you have your value props, attract developer talent using: (let them know who you are and the problem you solve) outreach (get them from zero to ship as quickly and smoothly as possible) technical enablement (show them what’s possible with your APIs, from the obvious to the extraordinary) inspiration Ultimately, Ash says, for developers to adopt your ecosystem you’ve got to be providing the right opportunities and developer experience for them. Smell like a developer “Developers have a strong nose for sniffing out what is authentically a developer product that will serve their needs well.” explains Roblox VP of Developer Relations . “Word of mouth is exceptionally strong with developer audiences so make sure you smell like a developer.” In other words, if you work in developer marketing or relations, be sure you’re representing your product in a way that’s technically credible and will resonate with an audience of developers. Grace Francisco Set a high bar for hires and don’t budge Development shops earn their reputation pretty quickly in a market. Former Twilio Developer Evangelist believes team quality is the biggest driver of that reputation. If you’re wise about hiring, eventually all the excellent technical ability in your company will form a gravitational pull that makes recruiting less difficult. Engaging high-caliber talent early in the hiring process will accelerate the attraction. Rob Spectre The more job candidates in the market your engineers engage with, the faster the word will spread about your team’s capabilities. Obstacles around compensation, equity, benefits, and even stack all evaporate when a candidate meets your engineers early and often. Simply put, great engineers want to work with great engineers. Ensuring you fill open positions with talent at every experience level supplies that demand. Place an emphasis on ongoing education and training opportunities “If a company doesn’t have some sort of support for engineers wanting to build their skills, then I’m not interested.” says , who’s worked as Clarifai’s developer evangelist. Providing classes, books, exploratory time, open source contribution opportunities and side projects are few ways your company can show employees and recruits that you value continued education. Cassidy Williams Hire for your values As an early employee of MongoDB, Senior Manager of Developer Advocacy has learned that much of an early-stage company’s culture depends on what founders do and how they do it. It’s important to be intentional. This means defining the positive values that will help your team be successful, then embodying and hiring for those values. If you believe that a strong sense of autonomy has helped you succeed in your career, live those values by allowing your whole team to fail fast and try new things on their own. If exchanging information with other developers was tantamount to your success, start an engineering blog or encourage teams to participate in open source. As you team grows, continuously communicate founder values by highlighting and promoting individuals who have done an exceptional job representing them. Francesca Krihely Set up the culture Zalando’s Open Source Evangelist offers five tips for building a strong tech culture that will attract great developers: Lauri Apple Offer an open source portfolio that reflects high quality, usefulness and innovation . allows teams to build happily and productively. However, agile frameworks aren’t sufficient. You must also reinforce agile values like collaboration, ownership, transparency and trust. Without these and mutual accountability, autonomy can easily transform into chaos. An agile infrastructure As a company, think about how you plan to retain the people you hire. Avoid c u u a i n as early as possible. Reduce the friction and paperwork around promotions and team-switching. Consider establishing and other programs that empower your people to stretch their abilities and explore their interests. And in the realm of open source, provide mechanisms that encourage developers to manage, grow and promote their projects; it will help them foster a product mindset and become well-rounded. Be inclusive and reward growth mindset. decency debt a c m l t o apprenticeships In doing so, you’ll more easily attract top talent. Bureaucratic, ego-driven environments stifle creativity and trust and lead to frustration. Hire competent and humble leaders . Trust enables your people to experience the level of necessary to debate, disagree, and communicate effectively. Build a trustworthy environment in which people can be themselves. psychological safety Be transparent From the highest levels on down, provide transparency into how decisions are made, why decisions are made, and what decisions need to be made. This doesn’t necessarily mean giving everyone a vote, Amazon Developer Advocate clarifies. What it means is making sure everyone gets a chance to understand and provide feedback on everything: from product to technology-related decisions. This transparency will help prevent people from feeling marginalized or “that they’re just ‘code monkeys’ in a feature farm” he says. Side benefit: it can also help ensure problems are looked at from multiple points of view. Chris Munns Get more advice on closing in-demand candidates . here

Tech evangelists reveal the secrets to attracting great developers

announced new in its image search in August, just a day after Pinterest updated its app to make the visual search feature front-and-center. This summer’s activity comes a few months after Pinterest announced , which allows a user to take a photo of an object with their phone and see related objects in the search results. It also closely follows Google’s May announcement of , which uses machine to do a real-time visual search using your phone’s camera lens. Google badges Pinterest Lens Google Lens learning Although Google and Pinterest lead in visual search, other ecommerce-focused tech companies have developed visual search as part of their search functions as well. This July, announced its plan to unveil two AI-powered visual search features for its mobile app, which will be coming out later this year. And way back in 2014, added an image recognition feature as part of its mobile app. eBay Amazon So far, the overwhelmingly obvious use case of visual search is for general ecommerce purposes. Companies want users to be able to search for and eventually purchase products through visual search. Some examples include large tech companies like Google or Pinterest building their own visual search functions, as well as legacy retail companies like that turn to visual search API/services startups like or . West Elm Clarifai Slyce In a world where a picture is literally worth a thousand words, it’s easy to see how visual search could help a user find a new pair of boots or a new sofa, and how this translates into a new era of search for the consumer. Understandably, general ecommerce, especially fashion and decor, are leading the way. Ecommerce industries like these have the most diversity and visual detail in their products. shows that 39 percent of its visual search queries were for apparel and 31 percent were for home decor. A study by Slyce Beyond the general use cases that are already pretty familiar, other spaces could benefit from adopting visual search moving forward. Some of these include the discount clothing industry, brick-and-mortar retail, and the fine art market. Discount clothing outlets In May, launched its own visual search tool (“Search with Photo”) to help shoppers find furniture. Part of the behind the new feature is to help Wayfair capture more sales from discount furniture shoppers. These shoppers can now upload photos of high-end furniture onto Wayfair to see whether the online store offers a similar but cheaper alternative. Wayfair strategy Companies from the traditional discount clothing industry could also leverage visual search to help consumers find their products. Companies in this space range from retail chains like Designer Shoe Warehouse and Burlington Coat Factory to fast-fashion ecommerce companies like H&M and Forever 21. For the latter category, the products are famously known for being of high-fashion garments that arrive on the shelves faster than the upscale originals. Adopting visual search seems like a natural move to help consumers search for high fashion looks at low prices. duplications Brick-and-mortar retailers A for Google Lens released after Google I/O 2017 shows a real-time visual search of a user’s surroundings from their phone. The user points their phone at a storefront and a Google Maps result for the storefront comes up. The result contains information about the particular storefront, such as its name, customer satisfaction rating, hours, etc. — much like the current search results in Google Maps. promotional video The combination of visual search with a map has the potential to draw online consumers to physical storefronts offline. It goes beyond the aforementioned example of real-time point-and-shoot search and can be used to enhance how paid search currently operates. For example, a retail brand could to alert a shopper that one of its products is available at a store near them. It’s a way for retail brands to expand their omnichannel strategy to include both online and in-store pickups. It could also allow purely brick-and-mortar small businesses to draw more local foot traffic. align its paid search Fine art galleries Describing a piece of fine art with keywords can be quite difficult — certainly more of a challenge than describing a blouse or a coffeemaker with keywords. In May, visual search API/services startup launched a visual search tool with , a digital image licensing platform for the U.K. visual arts space. Artimage’s new visual search tool gives users the ability to comb through 15,000 images of artwork in less than to quickly find unexpected gems. Visii Artimage 350 milliseconds Artimage isn’t the only one in the fine arts space looking to incorporate visual search into its business. , an art marketplace startup based in London, has similar ambitions. In the future, it’s easy to imagine a company like , which catalogs over , adopting a visual search tool to help its users discover artwork based on certain visual or stylistic preferences. Artfinder Artsy 1,800 commercial galleries The future of search is becoming increasingly visual. The current visual search adoption has primarily been dominated by large tech companies for typical ecommerce user cases. However, these three spaces — discount clothing, brick-and-mortar retail, and fine art — could really benefit from adopting visual search as part of their businesses. That’s worth keeping an eye out for. This piece was originally published in VentureBeat .

Visual search products like Google Lens could revolutionize online shopping

The long awaited of this series has arrived. It has taken me a significant amount of time longer to put together than I had anticipated. Now the wait is over, here it is… Part 2 of 3 This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series. Rise of artificial intelligence is rapidly taking over the world, especially countries like Anguilla; a small island in the Eastern Caribbean with the use of their .ai top level domain for companies within the AI and robotics industry. But in all seriousness, AI is without a doubt accelerating much faster than is probably known and much more established within our daily lives than most would realise. Conducting this research has demonstrated sheer scale of the landscape and progress of the artificial intelligence industry. “Already humans and AI are co-evolving and no one has paid attention to this yet…” “When we search on Google, it’s an AI deciding what we should see. When a dating site matches two people together there’s a machine learning algorithm… that is getting people together, that then have babies. And so in effect you have this machine learning algorithm that is breeding humans.”— Sam Altman, discussing AI . here In the case of the companies listed below, business and AI are co-evolving. With so many websites to go through and dissect there may be some descriptions or names that need changing. Please let me know if that’s the case or if you think there are companies I should add to the list. Shoutout to me , and I’ll get on to it! here Artificial Intelligence for businesses (enterprise intelligence): 🔉 Audio — language transcription and recognition — ? — transcribes insights from phone calls, video footage, and online — discover more customer and employee conversations** **— makes sound searchable — critical insights about your customers conversations — building blocks to add messaging, voice, and video to web+apps Capio Clover Intelligence Deepgram Gridspace MindMeld — advanced AI to power conversational interface Nexidia — turns customer interactions into valuable insights Pop Up Archive TalkIQ Twilio 📊 Business Intelligence & Analytics — optimise and autonomously IT and business operations** ** —behavioural AI for IoT — a suite of intelligent applications for enterprise — a range of products to improve enterprise products** ** — automates repetitive tasks within your business — making complex data science easy for enterprise Arago/HIRO Arimo Ayasdi DataRobot Dataminr — discovers events and breaking information before the news — helps you to answer questions about your business — a smarter Salesforce — adds intelligent decision making to web and mobile apps — helps you index, search, visualise and analyse your data — is a framework for temporal pattern recognition and prediction — to transform raw data into useful information automatically — helps you monitor server bugs Electra by Lore Einstein Fuzzy AI Logz.io NXT AI Paxata Poweredby.ai Sundown UBIX Ruths.ai — helps you do more with your data — a marketplace for analytics — captures, categorises, and extracts key information from all your data — fast data insights using search — solves big data better, faster and at less cost — find hidden value in complex human and business ecosystems — helps to optimise pricing Exchange.ai Owl.ai AnswerRocket iSeek.ai Ecosystem.AI Prix 🍎 Core AI — a common API for many algorithms, functions and models — workbench for neural networks — advanced industry specific ML for the enterprise — advanced machine learning for enterprise — advanced machine learning for enterprise — open source machine learning and deep learning platform — advanced machine learning for enterprise — deep learning as computational system — advanced machine learning for enterprise — advanced machine learning for enterprise — range of financial, ecommerce and digital marketing AI products — open-source deep learning and ETL for enterprise on the JVM — advanced machine learning for enterprise — log Analysis for enterprises Algorithmia Arya CognitiveScale Digital Reasoning Fluid AI H2O.ai Loop AI Labs Nervana Petuum Scaled Inference Sentient Skymind Vicarious Loom Systems PipelineAI — solves the problem of production ML and AI at scale — building AI using neuroscience Ogma 📋 Data Capture — marketplace to automate simple processes — automate the discovery of objects at scale — automatically prepare any data for immediate & compliant use — helps structure data from social media and blog — automatically extract web pages as structured data — extract data from almost any website — training data, image annotation and more for enterprise** ** — tools for operations team to automate business processes Amazon Mechanical Turk CrowdAI Datalogue DataSift Diffbot Import.io Playment WorkFusion 🔬 Data Science — single platform for all predictive use cases — training data, image annotation for machine learning teams — data science platform for prototype, deploy, and run at scale — enterprise data science platform for R&D and production** ** — helps you learn, work, and play with machine learning models — makes data science teams more productive — helps DS teams put machine learning models into production — a platform to build, test, and deploy AI algorithms** **— research engine, capable of discovering complex patterns in data — makes data unification of data silos possible — helps put data into useful structures for analysis — allows data scientists to deploy and update predictive models rapidly — automate the writing of reports, websites, emails, articles and more BigML CrowdFlower Dataiku DataScience Domino Data Lab — platform for collaborating, building and deploying — makes DS accessible to analysts with OpenSource algorithms Exploratory Kaggle RapidMiner Seldon SherlockML Spark Tamr Trifacta Yhat Yseop 💻 Development — detects business incidents — develop more adaptive, trusted and programmable AI models — helps predict project timelines — adds intelligent decision making to web and mobile apps — connecting projects with the right team — augments your coding environment with web available knowledge — deep learning platform for prediction and personalisation — makes developing chatbots for your business easy — make your bot smarter, by helping it self learn — rapidly web and mobile app testing — increase server uptime and predict downtime — project management and chat software that’s easy for teams AnOdot Bonsai Deckard.ai Fuzzy.ai Gigster Kite Layer 6 AI Morph Ozz RainforestQA SignifAI Turtle Improve.ai — automatically optimises app content, design, pricing etc. — gesture recognition for developers — trains deep learning algorithms to learn like human brain — abstracts away the complexity of ML libraries like TensorFlow for more effective management of AI models — helps with decision making for designers, engineers and leaders — helps analyse algorithm Gesture.ai Cognitive Toolkit Bonsai Tangle Imandra 📂 Internal Data — helps you work together, improve productivity, and data indexing** ** — helps employees with tasks and access data using micro apps — better website analytics Alation Cycorp — a range of different AI enterprise products — takes the pain of cluster management away to focus on DS — helps predict project timelines — insight into online trends and other text analytics tools — AI platform for business Databricks Deckard.ai Gavagi IBM Watson Kyndi — helps knowledge workers process vast amounts of information — SaaS AI for risk management and operations — makes your business software smarter oneFactor Probot Sapho Sofia eContext — structure for unstructured data — create intelligent interactions between people, devices, and data — deep insights into customers and internal data —automate decision-making at scale Hayley RelativeInsight Rainbird 🤖 Machine Learning —No-code end-to-end AutoML tool that takes you from data to predictions in minutes* *__ — a range of different AI enterprise products — helps you focus your digital ad on people you want to target — analyse data in the form of images, speech, text and video — now apart of the Uber AI Labs — can do menial task work, saving time for employees — platform to unite siloed data for better recommendations — improves machine learning models 100x faster PI.Exchange — develop more adaptive, trusted and programmable AI models__ Bonsai Cycorp Datacratic deepsense.io Geometric Intelligence HyperScience Nara Logics SigOpt Amazon Machine Learn — ML-as-a-service, amongst other things — import predictive models and scale infinitely to answer existential questions Providence 📡 Sensor (IoT/IIoT) — platform delivers real-time operational insights for industry — learns about shoppers behaviour in your store — helps to unify application development and data science — sensor analytics solution for businesses — machine learning for manufacturing process optimisation — a range of workflow optimisation products for fuel and industry — helps you develop, deploy, and operate industrial apps — helps renewable energy companies utilise their data better — manufacturing analytics — automates data engineering for data science — add a voice assistant to your connected product — platform to manage the development for your IoT applications — a predictive platform for major industries — smart building management for commercial buildings Alluvium Black C3 IoT KONUX Imubit Maana Predix Planet OS Sight Machine Sentenai Snips ThingWorx Uptake Verdigris 📚 Text Analysis/Generation — creates summaries from your text and information in real-time — extract meaning from your text and visuals — text analytics and mining which works without any training** **—automatic knowledge acquisition from text — build profile and understand your users better — scaleable text analytics software — capture, measure, and act on customer feedback — scalable API to automate text classification — interprets your data more useful information — helps you find errors and inaccuracies in documents — free open-source library natural of language processing in Python — automates information extraction, management and analysis — turn text into insights — helps improve how your job ads are written — automate the writing of reports, websites, emails, articles and more Agolo AYLIEN Compreno Cortical.io — advanced language processing fido.ai IntroSpect by Lore Lexalytics Luminoso MonkeyLearn Narrative Science Qeep spaCy Salient Stride Textio Yseop 😎 Vision — add instant text capture functionality to mobile apps and more! — automated drones with vision and diagnosis capabilities — analyses subtle facial expressions to identify human emotions — makes every camera in the world smart — helps cameras to detect analyse the video feed — finds defects in wind turbines — extracts and transforms data from handwritten and typed forms — helps you to organise media libraries — visual analysis for medical and transport industry — image detection for a range of uses and industries — brand and face recognition — makes satellite imagery useful — the first intelligent cloud video platform** ** — computer vision for automative and human detection — matches similar products and helps to suggests combinations — facial recognition — uses facial recognition to help you decided on job candidates — medical data analysis and interpretation — recognises different objects and things — dedicated brand recognition for social networks — satellite image analysis — deep-learning based computer vision platform — planet monitoring and analysis using satellite imagery — satellite image analysis — is an eye and emotion tracking platform — understands and describes video content — image analysis primarily for industrial purposes ABBYY Achron Affectiva Algocian __ __i Angus.a Birds.ai Captricity Clarifai Cortica Deepomatic DeepVision Descartes Labs Flixsense FotoNation GrokStyle Haystack HireVue Lunit Inc. Matroid Netra Orbital Insight Pilot AI Labs Planet Spaceknow Sticky.ai Valossa Vidi CloudSight — high quality understanding of images within seconds — detects foreign and intentionally placed objects for security — a range of intelligent computer vision techniques Irvine Sensors Pilot.ai Originally posted → liamiscool.com

A list of artificial intelligence tools you can use today — for businesses (2/3)

Meet He’s a genuinely nice guy who was created to help you maintain your diet. He keeps track of everything you eat and gives you statistics about it. @ItadakimasuBot. But he’s special — he can You don’t need to write down all nutrition info by your hand — he will do everything by himself. recognize food from your pictures. Note 1: This project was made with mainly learning purpose in mind, so it still requires a lot of work Note 2: in the first part of the article I’m going to explain what makes the bot work, so if you’re not interested in technical parts — skip it and start reading part 2, where I explain my view on the current situation with bots. Part 1. How @ItadakimasuBot works is a bot for (for now), written in pure . It uses Node.js for both Bot Logic and generation of Statistics page. @ItadakimasuBot Telegram JavaScript To recognize food I used API — even though it’s far from being perfect (it doesn’t recognize branded food, for example, both snickers and bounty will just be tagged as ‘Chocolate Bar’), it works good enough for now, so I decided against making my own system. Clarifai To get nutrition info, I found two possible choices: and . Both of them support full USDA information, but works with custom portions. FatSecret Nutrionix FatSecret I decided to stick with since, I think, information in it is in a bit more user-friendly form. Nutrionix Since bots don’t have built-in session, I decided to use for intermediate information, such as current bot state. Redis To keep long-term information, such as Food logs, Stats URLs and User information, I used Postgres. Besides Bot, I also have a server to handle showing statistics to user built. It’s built upon It’s actually really simple and only manages two different requests — one returns the *.html page, and one returns the data. Express.js. I didn’t use any fancy frameworks such as or for the front-end because, honestly, using big frameworks for a single page with 3 graphs would be ridiculous. Therefore, I just used (I didn’t want to use it, but since DatePicker of my choice uses it, I went with it), for graphs and for minimization. React Angular jQuery Chart.js Webpack Bot reacts to next commands: — Simply displays a message with a list of commands /help — Display daily norms and allows to change them /norms — Logs a food by the name /log food_name — Shows starting message, sets daily norms for the user and creates User objects /start — Generates and send User URL to see statistics /stats — Recognize food and log it [photo] In order to communicate with user I decided to use , because (modified OS keyboard) doesn’t allow to set custom key values, which would require either additional use of session, or additional API use. InlineKeyboard ReplyKeyboard Daily norms input flowchart Food logging flowchart Part 2. Why bots are useless. For now. Bots are fun to play with. You don’t need to install stand-alone applications, they work and look the same on any platform. Bots are easy and fun to develop. You don’t have to worry about GUI or platforms at all. But there are several serious flaws. First problem is that every simple action, such as the input of nutrition values, becomes really clumsy and requires additional validations — do you want to ask user to validate their info on every turn or only in the end? But what if user accidentally enters from info to one of the fields, should he re-enter everything? It could be solved by allowing to customize input methods, disabling normal keyboard and making custom input methods. As of now, there are two different input methods: and Both of them have their own pluses, but also they have their own problem. InlineKeyboard ReplyKeyboard. doesn’t hide normal input field, which requires making additional validations, in case user inputs something that’s not from the list. This is a bit solved by the fact that InlineKeyboard answer sends InlineQuery instead of normal Message event, but since the older InlineKeyboards aren’t disables, it doesn’t completely solves the problem. InlineKeyboard is used in place of a normal keybord and hides original from the user. Yet, original QWERTY can still be accessed. Another problem with it is that currently, it’s impossible to send custom information with it — the data sent by key will be exactly as the text on it, which would require additional use of either session or API. ReplyKeyboard Even without entering the nutrition info, a minimum of two validations is required Second problem is the lack of any visual control. Currently, it’s impossible to display graphs and tables inside the bot, there are only two choices: Generate graphs as images on the backend. Really bad choice because most of the bot users use mobile phones with small screens. Plus, it removes most of the graph informativity — it’s impossible to see the exact values. Send a URL to the web-page that has everything. It works, but then again, why do we need bots? Imagine looking at this on your mobile phone screen The bots of 2017 aren’t that different from the bots, that we had in IRC or Jabber 20 years ago. They have a really bright future, but as of now — it’s really hard to make them user-friendly for anything that’s more complex than controlling the lights in your house. Part 3. Future As of now, @ItadakimasuBot isn’t really meant to be used in daily life. I made it in several weeks and, as a student( ), can’t really afford working on it anymore. pls hire me But if I would, I’d: Port it to Facebook Messenger. Make my own Food-recognition system and nutrition DB Add support for different languages Add support for UK/US units (I might actually do this in near future) I made it open-source so if anyone wants to code-review it (as a aspiring programmer, I would love to have someone more experienced review my code!) or do something with it — you’re welcome. GitHub: https://github.com/nausik/ItadakimasuBot Bot: https://t.me/Itadakimasubot Thanks for your time!

How I made a food recognizing bot for Telegram and why it doesn’t have a future (yet)

The easiest way to “add AI” is to call an API from your code, whether that API is exposed by a local library or over the Web. software For example, this HTTP request sends a sentence to a sentiment analyzer over the Web: GET /api/sentiment?phrase=Airplanes+with+the+shape+of+hawks+are+cool Your code will get an answer like below, which means the sentiment of is positive. More precisely, the answer you get back is “this is an English sentence which has a positive (negative sentiments have a negative score) with a “sentiment strength” of 0.6, with 0 being weak and 1 being strong”. So if you are an airplane , may we suggest a hawk shape next time you have a clean sheet of paper out? designer {     "score": 0.6000000238418579,     "magnitude": 0.6000000238418579,     "sentences": [         {             "text": {                 "content": "Airplanes with the shape of hawks are cool.",                 "beginOffset": -1             },             "sentiment": {                 "magnitude": 0.6000000238418579,                 "score": 0.6000000238418579             }         }     ],     "language": "en" } If your application needs to work without an Internet connection, you can embed software libraries to call locally. For example, you can grab the to add language processing capabilities to your software, such as “part of speech tagging”, which tries to identify where are the nouns, verbs, adjectives, adverbs and so are in your text. Stanford CoreNLP toolkit Just as with regular programming, if you can’t find a pre-built Web service or library to do what you want, you’ll have to create your own special functions. These days, the most popular way to do this is by training a machine learning model with labeled data using something such as or (for a wide collection of machine learning techniques) or , , or (for deep learning models). We’ll walk through a few examples using a Web service called and Google’s TensorFlow later in this guide. scikit-learn Spark’s MLlib Tensorflow Keras Caffe2 MXnet Clarifai As of this writing (April 2017), high-level APIs are very easy to consume but there aren’t very many of them. Low-level APIs which allow you to build your own model are difficult to consume and there is a lot of art using them effectively. But the field is improving quickly and all the major public cloud providers (Amazon, Google, IBM) are working hard on making more high-level APIs and improving the tools you use to embed AI capabilities into your own code. API vs. Library vs. SDK When looking at what different companies/groups provide we will see there’s a lot of different acronyms and frequently overlapping acronyms used. Of these, the most common are: An API (Application Programming Interface) is a set of protocols, functions and definitions for building software. Traditionally an API defined abstractions: expected inputs and outputs for software and was implemented in concrete form in a . API. library Actual implementation of an API, a library is generally embedded within the code that is using the API, but it may be separate and shared by multiple applications; these are called and are widely used by Operating Systems like Windows and MacOS to provide developers access to different types of services. Library. dynamic shared libraries SDKs (Software Development Kits) are generally considered in some way higher level than simply libraries, perhaps including multiple libraries, or different tools to make use of libraries easier, multiple language bindings, or so-called “wrappers” that can hide from programmers some of the complexity involved in using the library, by trading off simplicity for flexibility. SDK. As a starting point, then, we can think of an API is a set of definitions, protocols, functions and capabilities, which are then implemented in one or more libraries which can then be released as part of an SDK. The caveat is that this is a good starting point to think about these terms, but not an absolute. In particular, “API” has become a somewhat overloaded term. For example, a company may provide a “Speech API” and then supply additional libraries, bindings or SDKs that can be used with different environments and languages. What makes the “Speech API” in this case different than the more abstract version we discussed above is that it can be used directly without any intermediate libraries by making specially formatted HTTP requests; similar to the requests used by web browsers to obtain the data they use to render web pages. via a web service Most of these APIs use (a few use XML) as a standard to transfer data back and forth. This makes it easy to use (and, if necessary, create) wrappers in different languages. JSON The pseudo-code example above follows a common pattern: call an API via REST, passing text, audio, images, or video, and receive back a JSON dictionary or array that contains the results of the analysis performed by the remote service. The downside? They may be too inflexible for your needs, in which case you’ll need to delve more deeply into training your own models, as we’ll see later. Fundamentally, these APIs are high level, fast, and straightforward to use. Pricing for Web APIs is usage-based and generally reasonable. They have good availability and uptime, although they rarely give you SLAs or other guarantees as to how they will perform. Many are in various development states: “beta,” “alpha,” “preview,” and so on. As a final note in terms of compatibility or standards: there aren’t any. There’s really no direct way to “port” a system that uses one API to another without having to modify the requests and the response processing. On the other hand, the calls and responses are, as we can see, fairly straightforward, so switching services is not hard for small systems. Originally published in ’s . Andreessen Horowitz AI Playbook is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising & sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Giving Your Software AI Superpowers

Machine is a hot topic nowadays. It is a kind of a buzz word everyone is trying to use to look smarter. I asked my friend about type of algorithm his company is using for classification and he said: Learning “ To be honest I don’t have ML experience. I’m tech guy. But to sound smart, I would probably said: we are using complex machine learning algorithm based on artificial neural “ network Actually, it sounds quite good :-) To help people fill hunger for machine learning knowledge I will post series about Machine Learning — and predictive analytics in particular. Machine Learning according to : wikipedia is the subfield of that … gives “ .” Evolved from the study of and in , machine learning explores the study and construction of that can learn from and make predictions on ... Machine learning computer science computers the ability to learn without being explicitly programmed pattern recognition computational learning theory artificial intelligence algorithms data To make it simple, machine learning allows you to build an algorithm (recipe) that doesn’t have to be explicitly defined — is it possible? Yes, everything in this algorithm (recipe) is learned from your data — each condition or new step is based on your data. As a result of learning you get an algorithm, but to be more precise, you obtain a . This model has some knowledge about your data and this model can serve responses for you based on input data. model There will be always some kind of response from model, because otherwise it will be useless. Response can be in different formats — it can be numbers, images, audio, or response can be the internal structure of the model which can bring us some insights. Based on the response you can take actions. Usually, there are two stages in machine learning model life: — in this step you need to learn your model, you are showing data to your model and train it. Phase 1: Learning — it is a ‘production’ phase, in this phase new data is presented to your model, and it serves responses — based on its previous knowledge (from phase 1). Phase 2: Prediction You cannot omit learning step for your model, because otherwise it will produce some random responses. However, sometimes you can buy access to already learned model, which you can use for predictions (for example , ). clarifai algorithmia There are many ways you can categorize machine learning algorithms, one of them depends on data available for learning: — training with a teacher (supervisor), your data should contain information that will be used to guide model about response values, you know what values are desired as the response, and you will teach your model to return them. Some examples: SPAM classification in emails, objects recognition in images, credit risk assessment, churn prediction, prices prediction on stock market. supervised learning — there is no information available to guide model about desired response value, model need to figure out it by itself — it won’t be supervised. Some examples: user segmentation, complex data visualization. unsupervised learning — during learning phase your model interacts with dynamic environment in which it has a certain goal, can make actions and is obtaining a feedback (rewards and punishments) from environment. Some examples: self-driving cars or playing game against opponent. reinforced learning In my opinion , so right now after some theory we will jump into classification task (which is part of ). OK, let’s learn our model! the best way to learn humans is by doing supervised learning I’ve prepared several data sets that are good (easy) for start. They are available on . We will use Adult data set: my github Link for data: here original data source: here In this task, we are training a model to predict whether person’s income exceeds $50k per year based on census data. Let’s look into our : dataset Few top rows from Adult dataset. There are 15 columns in our dataset and 32,562 rows. The first row it is a header and it describes meaning of each column. One row describes one person. Each person is described with: age (continuous attribute) workclass (categorical attribute) fnlwgt (continuous attribute) — it is some index that describes person education (categorical attribute) education-num (continuous attribute) marital-status (categorical attribute) occupation (categorical attribute) relationship (categorical attribute) race (categorical attribute) sex (categorical attribute) capital-gain (continuous attribute) capital-loss (continuous attribute) hours-per-week (continuous attribute) native-country (categorical attribute) income (categorical attribute) Continuous attribute means that values in that column are numbers, for example “age” — in this column we will see only numbers. Categorical attribute means that in this column there will be strings, for example “sex”, there are values “male” or “female”. String values will be transformed into numbers (we will not go into preprocessing details right now, it will be covered in next lessons). For building classifier we will use , which has easy web-based UI — you can set up account there and receive free credits for start (enough to run experiments from MLJAR Academy lessons). We start analysis with creation of new project. MLJAR After project creation please go to and add new data source. Sources After adding new dataset, please go to and let’s check data, column types and distribution. Preview Before training, we need to select which attributes will be used as input for a model (‘Use it’ columns) and which column is a ‘Target’ column. The target column will be used in model training to , I will sometimes call column as column, because model will learn to give reposnses (output) similar to target value. After selecting column usage we need to ‘Accept Attributes Usage’ at the top of . Now, we are ready to start our first Machine Learning experiment! Let’s go to and add new experiment. supervise target output Preview Experiments Wow! This dialog has a lot of features — don’t worry, many of them are set to smart defaults! In this dialog we will set three things: Input dataset: it will be our data source Learning Algorithm: we will select Random Forest Metric to be optimized: we will set Area Under ROC Curve That’s all! We click ‘Create & Start’ and the model training will be started (in background we launch AWS instances for you that will do the job). Please don’t worry about other experiment’s parameters, for example: preprocessing. We will go into details in next lessons. After experiment creation you will be redirected to view. Results OK, what’s going on? There are several rows in the results table and each represents one model. You can click on row in the table to see model parameters. When you train machine learning algorithm it usually has many parameters that control the learning process — their values need to be carefully selected and MLJAR do this for you. That’s all for good start! You made a great progress today: you know what is Machine Learning you know what are types of learning you trained your first classifier with Random Forest algorithm In next lessons we will cover more details: hyper-paremeters tuning data preprocessing model validation and many more! so please subscribe at mljar.com to not miss next posts I’m really interested on your feedback on MLJAR platform! Disclaimer: some of the definitions were simplified to make it easier to explain and understand.

MLJAR Academy: Start with Machine Learning

There’s been a lot of talk about women in tech these days. Headlines include both the repugnant and up-lifting. One the ugly side, we’ve all read about the sexual harassment and mistreatment at famous . On a more uplifting note, more s and are celebrating women’s contribution to tech — and to take up careers. tech companies campaign movies inspiring future ladies STEM For International Women’s Day, we decided to tackle this issue with APIs. We built a project that determines how many of Stack Overflow’s top-ranked 3,479 users are women. We collected this data by connecting to public APIs (Stack Overflow and Clarifai’s Visual Recognition API) through . RapidAPI How We Got the Numbers Since connects to multiple APIs through one abstraction layer, finding this data was relatively easy. Here are the APIs that we used through RapidAPI. RapidAPI Stack Exchange API We exported the top 3,500 Stack Overflow users with the Stack Exchange API. We sorted users based on Stack Overflow . A user gains or loses reputation by, in , “posting good questions and useful answers.” reputation Stack Overflow’s words Clarifai API We used the to determine the gender of the user based on their profile picture. The Clarifai API works by returning a probability that the face in the image is a masculine or a feminine. When the API could not find a face in the image, we categorized the result as unclassified. Clairfai Visual Recognition API’s Demographic model Now, onto the results! The Results You can dig into the for yourself, but here are the key stats and some additional analysis. raw data Out of the top 3,479 profiles, the visual recognition API classified 1,515 profile images with a discernible gender. Out of the profiles with a discernible gender, the visual recognition API classified 5.6% as feminine and 94.4% masculine. The top 100 Stack Overflow users consist of 77 masculine profiles, 21 unclassified and 1 feminine profile. In total, the visual recognition API classified 1,428 profiles as masculine, 84 as feminine and 1,964 as unclassified. Finding 0: Most Stack Overflow profile pictures are gender neutral The vast majority of Stack Overflow profiles had a gender neutral image or icon as a profile picture, which made it trickier to categorize. . Out of the top 3,500 profiles, 1,512 had a profile image with a discernible gender To give you some context, here are some example profile pictures that the API would (obviously) not classify with a gender. Finding 1: There is only one woman in the top 100 Stack Overflow users Since the is still in alpha, we went through the top 100 results manually as well. We found Unclassified users either had gender neutral profiles (see Finding 0), did not provide enough information to determine gender or identified outside the male/female gender binary. One user even identified as non-human! Clarifai Demographic model 77 masculine profiles, 21 unclassified and 1 feminine profile. Finding 2: The Clarifai Demographic Model is still in Alpha The Clairfai API’s Demographic model is pretty fun to play with — you can test a call in your browser through the . However, we noticed the model is definitely still in alpha. For example, this Seminoles fan’s profile picture was returned as “feminine.” RapidAPI package The API may have some limitations when it comes to illustrated characters or icons. Conclusion & Next Steps Determining gender on Stack Overflow turned out to be trickier than we initially anticipated. Many users favor icons or illustrations over profile pictures, which made it more difficult to identify gender. To get better results, a iteration of this experiment could include the , which determines a probability that a name is associated with a masculine or feminine gender. Accuracy improves with location, so we exported that data as well. future Gender API

Only One Woman in Top 100 Stack Overflow Accounts

Unsupervised Investments (I): A Guide to AI Investors

Machine vision captured tag cloud from the city of Tartu, Estonia How Teleport uses machine vision to peek into every street corner I’ve teased my readers before on how . Just like online dating sites are letting AI decide who gets to make babies with whom, we’re letting AI decide who’s moving where. So… AI will reorganize the human population How exactly do we decide what’s a good city these days? We read the news, hear good things from our friends, photos and videos, get the best job offers from there, and quite often, we often just go and the city. see see I’m not going to get into how AI is involved in deciding which news, friends’ posts, videos or photos of cities we get to see, but I am going to shed some light into how we use AI at to you . And since I’m obsessed with convincing , then I’m going to walk through the process with simple code examples to illustrate how easy it is to get started playing with AI with all the tools out there already. already Teleport help see cities more objectively every kid to start coding The following ideas were mainly inspired by MIT’s . StreetScore project Visiting cities through the eyes of AI An obvious benefit of using software to visit places is that Newton ain’t got nothing on software bots and they can accelerate their way through in a heartbeat without worrying about their mass (in case you did not know — is a well known time unit measure under International System of Units). thousands of places heartbeat So let’s grab the borders of Tartu, Estonia (my university town) from the and generate random geographical coordinates to visit. Why 10K you might ask? Because ! Teleport Developers API 10 000 science Such uniform distributions over geographies would of course leave our perception of the city quite biased, as most of the time we would end up in the woods or on top of roofs with little to see. But luckily with the help of we can bias those random locations out from the middle of nowhere and get something more like this: Google Maps API As you can see, the locations (red dots) we plan to visit are more or less on roads and streets where we hope to see more action than watching trees grow. Now that we have our locations, it’s time to get into the bit and get acquainted with the . Essentially I’m calling their API with my coordinates and saving the resulting image locally. seeing Google Street View Image API After some reverse geocoding magic and religious following of Google’s API quotas, I’m actually getting photos returned for each coordinate (as opposed to that you’d get with just uniform distribution). “Sorry, we have no imagery here.” Here’s a peek into of Tartu my code has access to without leaving the room (or my computer?). thousands of images What’s on the image HAL? This is where the AI bit comes in. To keep things simple, I’m running our set of images through publicly available . There are plenty of choices out there like machine vision APIs Microsoft Computer Vision API Google Cloud Vision API IBM Vision Recognition API Cloud Sight API Clarifai etc. but for this example I went with Microsoft’s I’ve always been impressed with the Microsoft Research Organization and to be honest, their API free use terms are the most favorable. They’ve even been kind enough to provide to get you going. Oxford Project. quick start code in Python In essence you just upload an image to their API and get back a line of text describing the scene. Here’s an example of an image from Tartu along with the output text from MS Vision API. Description: “ ” A motorcycle parked in front of a building Compression is intelligence With their API, I mapped 8.5GB of raw image pixel data from Tartu into 255KB of text data (object space). If you’ve ever looked into the relationship of , then perhaps this size reduction speaks volumes. compression and intelligence 35294x Anyhow, now that we finally have natural language descriptions of the scenes, we can start looking into statistics to see if if we can find something that could possibly speak into someones decision to move. 27 of 1528 most frequent terms of machine vision based analysis of 10K images of Tartu, Estonia I almost fell asleep reading the list until I read “motorcycle”. The amount of motorcycles or more specifically their proportion to the population speaks very much into how a city potentially is and that is something that resonates with me personally (I own two motorcycles). motorcycle friendly Surely there are other sources for finding statistics about motorcycles in various cities, but bare in mind that they typically come from different sources for different countries and would require a substantial amount of work to capture and normalize across hundreds of major urban areas. The beauty of Google Street View is how well it covers big cities as a and how well it lends itself to capturing visible in images. The premise is that … single data source any knowledge If You can see it, then AI can see it better If not today, then surely tomorrow. as Paul Graham has eloquently . Advances in AI and more specifically Deep Neural Networks are catching us by surprise every week and if you’re still in doubt about their potential for extracting more information from images than humans can, then consider this list of recent achievements in : “Live in the future, then build what’s missing” said AI beating humans Google’s AI wins fifth and final game against Go genius Lee Sedol Carnegie Mellon Artificial Intelligence beats top poker pros Microsoft AI beats humans at speech recognition etc. Motorcycles of course are just a toy example of something I personally care about, but it’s not hard to imagine extracting any knowledge that humans can see (or perhaps don’t even notice) from the images. There are many clues to objective measures describing hidden in the millions of images captured by Google. quality of life Here’s a quick way of sanity checking if the idea has any merit by checking something we know to be true. Capture another 10K images for Amsterdam and count the occurrence of bicycles in the image tags: Without regard to population density (or actually counting how many bicycles are on each image) at least the general intuition seems to be validated. From counting bicycles and children to mapping out neighborhoods with run down buildings and graffiti, the opportunities to translate this massive source of image data into ideas that are plentiful. shape our moves Getting Off the Streets Certainly not everything we might care about is visible from the streets. Sometimes you have to go off-road. Here’s an example of some work Tanel Pärnamaa did when interning at Teleport where he took a set of public geo tagged photos from Flickr and used machine vision tagging to . Funnily enough he had a lot of sandy golf courses show up when only limiting the signal to the machine vision outputs. identify beaches Beaches as identified by AI in Flickr photos by Tanel Pärnamaa Even more so, with Planet just to orbit, we have another amazing image source that we can let AI loose on. The amount of data out there is growing exponentially and so is our understanding of life quality in cities. launching 88 more satellites Choose your job, choose your city, choose your life! One of our investors has that said “The spread of computers and the internet will put jobs in two categories: people who tell computers what to do, and people who are told by computers what to do.” I’ve seen incumbents in our space spending money on hands-on-the-ground teams to capture something that could be done with . I’m convinced that the efficiencies gained through and crowd-sourcing are giving us a substantial competitive advantage, and I’m eager to see how it all plays out in the long run. code machine learning In the mean time, for those of you more inclined to , here are some example challenges of the approach described above: tell computers what to do how to distribute your agents/probes across geographies while minimizing various biases (introduced by urban area size, population density, etc ) how to normalize captured data across urban areas to facilitate fair comparisons how to avoid double counting objects present in close scenes? how to reduce the effects of weather, season, daytime etc. in said statistics? how to optimize the viewing direction, sampling or combining of images in each coordinate to take advantage of 360 degree views? how to build/train models with a specific goal of extracting quality of life data from images (as general purpose classifiers exemplified above will only get you so far and are meant more as inspiring examples)? And lastly that could be captured from public image data that would correlate with quality of life and could potentially influence your decisions to move somewhere? Come share your thoughts on ! what cool things can you think of twitter Don’t forget to click the heart below or share on social if you liked what you read Silver Keskküla on Twitter Find your best city with Teleport

AI can help you choose where to live

When I built my first company starting in 1999 it cost $2.5 million in infrastructure just to get started and another $2.5 million in team costs to code, launch, manage, market & sell our software. So it’s unsurprising that typical “A rounds” of were $5–10 million. We had to buy Oracle database licenses, UNIX servers, a Sun Solaris operating system, web servers, load balancers, EMC storage, disk mirrors for redundancy and had to commit to a year-long hosting agreement at places such as Exodus. venture capital - Mark Suster, in his post “Understanding Changes in the Software & Venture Capital Industries” Over the course of the last 12 years or so, we’ve seen an evolution from large traditional VC firms investing $5-10M per company in the first round of financing to the emergence of “micro” VC firms investing in rounds $1M-$3M dubbed “seed rounds”. This evolution has also spawned even smaller firms investing in rounds of several hundreds of thousands of dollars as well in a stage referred to as “pre-seed”. As Mark Suster wrote in his post linked above, the emergence of open source software and cloud computing completely eviscerated the costs and barriers to starting a company, leading to deflationary economics where one or two people could start their company without the large upfront costs that were historically the hallmark of the VC industry. These lower barriers to entry has led to a “ ” of startups but hasn’t necessarily changed the rules of business. Without a defensible moat, it’s just about impossible to create a large company with sustainable profits. cambrian explosion The reason to take VC money isn’t to start a business but rather to deploy capital today at a loss in order to generate significant profits in the future, hence the . This delineation has become blurred, especially because it seems lost on some that in the past, upfront costs in some cases created a barrier to entry since capital wasn’t nearly as abundant as it is today. power law However, the same technology platforms (mainly cloud computing) that created the lower barrier to starting up previously, may also be providing an opportunity to build a moat today. In this scenario, startups can raise large amounts of money early on, not for servers and databases but rather to collect the necessary data to improve their algorithms in order to create a defensibility over the long term. Consider the following company financings: In June 2014, raised a $45M Series A round, the first round of financing according to Crunchbase Affirm After raising a $2.1M seed round in May 2014, raised a $9.2M Series A round in January 2015 (a total of $11.3M in 8 months time) x.ai In April 2015, raised $10M in the first round of financing according to Crunchbase Clarifai After raising a $1.5M seed round in February 2015, raised an $8M series A round in December 2015 (a total of $9.5M in 10 months time) Textio Just last week, announced a $6.5M seed round Hangar Technology What do all of these companies have in common? If your first thought is that they all harness either , artificial intelligence or computer vision to their advantage, you’d be right. However, at its core the benefit to machine learning is the positive feedback loop it provides which is commonly referred to as . machine learning data network effects It is likely that over the long term algorithms will become a commodity. Since the real value then is in the proprietary data set collected, a startup is at a disadvantage on day 1. This is where first mover advantage actually matters. As a startup collects the data necessary to feed their ML algorithms, the value the product/service provides improves, allowing them to access more customers/users that provide more data and so on and so forth. This extra capital can be deployed in numerous ways depending on the data set the startup is looking to collect and the company’s strategy and target market. I met with a company a few weeks ago that was raising money in order to create a physical studio that would allow them to set up cameras and sensors to collect visual data on the human body. The data they collect will allow them to build a computer vision-enabled body scanner that can understand the different features and nuances of the human body. For Affirm, as Max Levchin points out on , the company will lose money in the beginning (the base rate for the credit card industry can be as 50% of all the capital they lend early on). However, over time as Max states, the only way to build a defensible business in the category is to learn from your own data and to collect enough of that data that your underwriting continues to improve over time to a point where the business will become more and more profitable as time goes on. So, by raising a large amount of money early on, Affirm can acquire customers to ramp up transaction volume and absorb the impact of these early losses in order to collect a data set proprietary to them. this podcast I’m not privy to the strategy all of these companies have/will rely on to collect unique data sets but it wouldn’t be too much of a stretch to make assumptions about some possibilities. For example, x.ai launched a free beta product to users and rolled it out slowly. I’d assume that this was so that their “AI trainers” could review the product’s interactions and label the training data to improve the product for future users. By rolling the product out to more users and continuing to learn from the labelled training data, the algorithms could continue to improve until the product could get to a point where it could be deployed openly to the public and one that users might pay for. Another point worth making is that collecting real world data is increasingly a hardware problem which could lead to even larger rounds (examples include autonomous vehicles, robotics, drones, etc). If the datasets collected early on at a loss can provide large profits in the future, then it makes sense why many of these companies either raise a large amount of capital at the seed stage or go out to raise a larger round of financing within a year of the first round of funding. I think we’ll see more rounds of this nature over the course of the next few years as we generate more data and the use cases for harnessing that data becomes more apparent. So while startups leveraging data to build a superior product may need to raise more money early on to get the company off the ground, it may also lead to superior returns without raising massive amounts of capital across stages named for the back end of the alphabet. If this is the case, then the throwback to the early days of VC would be a welcome one. Thanks to Mike Dempsey for reading and providing feedback on this post. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . 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