Jhansi, a city steeped in history and valor, is famous for its connection to Rani Lakshmibai and its role in India’s first war of independence. Whether you’re visiting Jhansi to explore its rich heritage or planning a weekend getaway, having your own transportation is key to making the most of your trip.

Motoshare has now launched its bike and car rental services in Jhansi, offering a convenient way to explore the city and its surroundings. With Motoshare, you can easily book bikes and cars online directly from trusted local vehicle owners, ensuring a smooth and reliable travel experience.

Start your journey today:

• Car Rentals in Jhansi
• Bike Rentals in Jhansi

Why Choose Motoshare in Jhansi?

1. Seamless Booking Process
   Renting a bike or car through Motoshare is quick and hassle-free. Simply browse, select, and book your preferred vehicle online.
2. Wide Range of Vehicles
   From scooters for city rides to cars for family outings, Motoshare offers a variety of options to suit your needs.
3. Direct Rentals from Owners
   Connect directly with local vehicle owners for affordable and trustworthy rentals.
4. Flexible Rental Plans
   Choose from hourly, daily, or weekly rentals based on your travel requirements.
5. Transparent Pricing
   No hidden charges—enjoy clear and competitive pricing for every ride.
6. Eco-Friendly Options
   Opt for electric bikes and cars to reduce your environmental impact while exploring Jhansi.

Top 10 Places to Visit in Jhansi with Motoshare

Jhansi is a treasure trove of history, culture, and scenic beauty. Here’s a curated list of must-visit attractions you can explore with your rented bike or car:

1. Jhansi Fort
   • Highlights: A historic fort that narrates the bravery of Rani Lakshmibai.
   • Best Time to Visit: Winter mornings for a comfortable exploration of the fort.
2. Rani Mahal
   • Highlights: The former residence of Rani Lakshmibai, now a museum showcasing artifacts and sculptures.
   • Best Time to Visit: Morning hours for a peaceful visit.
3. St. Jude’s Shrine
   • Highlights: A serene Catholic church known for its beautiful architecture and tranquil ambiance.
   • Best Time to Visit: Early mornings for a spiritual experience.
4. Barua Sagar Lake
   • Highlights: A picturesque lake surrounded by historical ruins and temples.
   • Best Time to Visit: Sunset for stunning views and a relaxing atmosphere.
5. Maha Lakshmi Temple
   • Highlights: A historic temple dedicated to Goddess Lakshmi, showcasing stunning carvings.
   • Best Time to Visit: During Diwali for special celebrations.
6. Orchha (near Jhansi)
   • Highlights: A nearby town with majestic palaces, temples, and scenic views along the Betwa River.
   • Best Time to Visit: Winter months for pleasant weather.
7. Jhansi Museum
   • Highlights: A museum that houses artifacts from the Bundelkhand region and details the history of Jhansi.
   • Best Time to Visit: Daytime to explore at leisure.
8. Herbal Garden
   • Highlights: A peaceful garden featuring medicinal plants and lush greenery.
   • Best Time to Visit: Morning hours for a refreshing walk.
9. Parichha Dam
   • Highlights: A scenic spot perfect for picnics, boating, and relaxation.
   • Best Time to Visit: Early evenings for cool breezes and tranquil views.
10. Ganesh Mandir
    • Highlights: A temple dedicated to Lord Ganesha, known for its religious significance and vibrant atmosphere.
    • Best Time to Visit: Tuesdays for special prayers and rituals.

Plan Your Jhansi Adventure with Motoshare

Jhansi’s historical landmarks and cultural richness make it a fascinating destination for travelers. With Motoshare’s bike and car rental services, you can explore every corner of this iconic city with ease and convenience.

Book your ride now:

• Car Rentals in Jhansi
• Bike Rentals in Jhansi

Travel at your own pace, enjoy the freedom of choosing your itinerary, and make your Jhansi trip truly memorable with Motoshare!

The post Rent Smarter in Jhansi with Motoshare’s Platform appeared first on Artificial Intelligence.

Aligarh, famously known as the “City of Locks,” offers a blend of rich history, educational heritage, and cultural vibrancy. Whether you’re a traveler exploring the iconic landmarks or a local resident planning a short trip, having your own ride ensures convenience and freedom. With Motoshare’s newly launched bike and car rental services, traveling in Aligarh has never been easier!

Motoshare provides a user-friendly platform where you can book vehicles directly from trusted local owners. With just a few clicks, you’re ready to explore Aligarh on your terms.

Start your Aligarh adventure today:

• Car Rentals in Aligarh
• Bike Rentals in Aligarh

Why Choose Motoshare in Aligarh?

1. Seamless Booking Process
   Renting a bike or car is quick and easy. Browse, book, and ride—all from the comfort of your home.
2. Wide Range of Vehicles
   From scooters for city commutes to SUVs for family outings, Motoshare offers a variety of options to suit every need.
3. Direct Rentals from Owners
   Motoshare connects you directly with vehicle owners, ensuring affordable prices and reliable service.
4. Flexible Rental Plans
   Choose hourly, daily, or weekly rentals to match your travel plans.
5. Budget-Friendly Options
   Transparent pricing with no hidden costs ensures you get the best value for your money.
6. Eco-Friendly Choices
   Opt for electric bikes and cars to explore Aligarh sustainably.

Top 10 Must-Visit Places in Aligarh with Motoshare

Aligarh is a city steeped in history and culture, making it a delightful destination to explore. Here’s a curated list of the top attractions to visit with your rented bike or car:

1. Aligarh Muslim University (AMU)
   • Highlights: A prestigious educational institution with beautiful architecture and lush green campuses.
   • Best Time to Visit: Anytime during the day for a peaceful stroll.
2. Aligarh Fort
   • Highlights: A historical fort known for its unique architecture and historical significance.
   • Best Time to Visit: Morning hours to explore comfortably.
3. Shekha Lake
   • Highlights: A serene spot ideal for picnics and bird watching.
   • Best Time to Visit: Winter mornings for the best bird sightings.
4. Khereshwar Temple
   • Highlights: A spiritual site dedicated to Lord Shiva, surrounded by tranquility.
   • Best Time to Visit: Early mornings for spiritual peace.
5. Sir Syed Academy Museum
   • Highlights: A museum showcasing the life and legacy of Sir Syed Ahmed Khan, the founder of AMU.
   • Best Time to Visit: Daytime for a deep dive into history.
6. Dor Fortress
   • Highlights: An ancient site offering a glimpse into Aligarh’s historical roots.
   • Best Time to Visit: Morning or late afternoon for cooler weather.
7. Jama Masjid
   • Highlights: A majestic mosque known for its intricate carvings and historical importance.
   • Best Time to Visit: Early mornings or evenings.
8. Teerthdham Mangalaayatan
   • Highlights: A peaceful Jain temple complex with stunning architecture.
   • Best Time to Visit: Early mornings for a tranquil experience.
9. Sky High Adventure Club
   • Highlights: An adventure spot for those seeking thrills like ziplining and trekking.
   • Best Time to Visit: Daytime for outdoor activities.
10. Shopping in Center Point Market
    • Highlights: A bustling market for local crafts, street food, and souvenirs.
    • Best Time to Visit: Evenings for vibrant energy and delicious snacks.

Plan Your Aligarh Journey with Motoshare

Aligarh’s charm lies in its blend of history, culture, and modern vibrancy. With Motoshare’s bike and car rental services, you can explore the city comfortably and conveniently.

Book your ride now:

• Car Rentals in Aligarh
• Bike Rentals in Aligarh

Enjoy the freedom to travel at your own pace with Motoshare, and make your Aligarh experience truly unforgettable!

The post Why Motoshare is Aligarh’s Go-To Rental Platform appeared first on Artificial Intelligence.

Vadodara, also known as Baroda, is a city steeped in cultural heritage and architectural splendor. Whether you’re planning to explore its historical landmarks, lush gardens, or vibrant markets, having the freedom to travel at your own pace is essential. That’s where Motoshare, your trusted bike and car rental platform, comes in!

Motoshare’s newly launched services in Vadodara provide a seamless way to rent bikes and cars directly from local vehicle owners. With just a few clicks, you can select your preferred ride and embark on your Vadodara adventure.

Explore the convenience of renting through Motoshare:

• For car rentals in Vadodara
• For bike rentals in Vadodara

Motoshare Services: Simplifying Rentals in Vadodara

1. User-Friendly Platform
   Motoshare’s intuitive interface allows you to search, compare, and book vehicles effortlessly.
2. Direct Rentals
   Book bikes and cars directly from trusted local owners, ensuring cost-effective options and transparent pricing.
3. Variety of Vehicles
   Choose from a range of scooters, bikes, and cars to suit your travel needs, whether it’s a short city ride or a day-long excursion.
4. Flexible Rental Plans
   Opt for hourly, daily, or weekly rentals based on your schedule and preferences.
5. Affordable Rates
   Enjoy competitive pricing with no hidden costs, making your travel budget-friendly.
6. Eco-Friendly Options
   Go green with electric bike and car rentals, contributing to a cleaner environment.

Top 10 Places to Visit in Vadodara with Motoshare

Vadodara offers a delightful mix of history, culture, and natural beauty. Here’s a curated list of must-visit attractions to explore with your Motoshare ride:

1. Laxmi Vilas Palace
   • Highlights: One of the grandest palaces in India, showcasing Indo-Saracenic architecture.
   • Best Time to Visit: Morning hours for guided tours and photography.
2. Sayaji Baug (Kamati Baug)
   • Highlights: A sprawling garden with a zoo, planetarium, and toy train rides.
   • Best Time to Visit: Early morning or late afternoon for a relaxing stroll.
3. Baroda Museum and Picture Gallery
   • Highlights: Rare artifacts, sculptures, and European paintings.
   • Best Time to Visit: Mornings for an in-depth exploration.
4. Champaner-Pavagadh Archaeological Park
   • Highlights: A UNESCO World Heritage Site with ancient forts, mosques, and temples.
   • Best Time to Visit: Winters for pleasant weather and a full-day exploration.
5. Kirti Mandir
   • Highlights: A royal cenotaph built in memory of the Gaekwad rulers.
   • Best Time to Visit: Afternoons for quiet reflection.
6. Ajwa Water Park
   • Highlights: Thrilling water rides and recreational activities for families.
   • Best Time to Visit: Summer months to beat the heat.
7. Sursagar Lake
   • Highlights: A serene lake with a massive Shiva statue at its center.
   • Best Time to Visit: Evenings for a tranquil atmosphere and illuminated views.
8. EME Temple
   • Highlights: A unique temple made of aluminum, dedicated to Lord Shiva.
   • Best Time to Visit: Early mornings for a peaceful darshan.
9. Nyay Mandir
   • Highlights: A beautiful example of Byzantine architecture that serves as a courthouse.
   • Best Time to Visit: Daytime for architectural admiration.
10. Hathni Mata Waterfall
    • Highlights: A picturesque waterfall surrounded by lush greenery.
    • Best Time to Visit: Monsoon season for the most spectacular views.

Why Choose Motoshare for Exploring Vadodara?

1. Freedom and Flexibility
   With a rented bike or car, explore Vadodara at your own pace without worrying about public transport schedules.
2. Trusted Rentals
   All vehicles are sourced from verified local owners, ensuring quality and reliability.
3. Budget-Friendly Options
   Motoshare offers affordable rentals without compromising on service quality.
4. Customizable Plans
   Tailor your rental duration to your needs, whether it’s a short trip to Sayaji Baug or a long drive to Champaner.
5. Travel with Ease
   Motoshare’s platform eliminates the hassle of paperwork, providing a seamless booking experience.
6. Support Local Communities
   By renting directly from vehicle owners, you contribute to the local economy and gain insider travel tips.

Plan Your Vadodara Adventure with Motoshare

Vadodara is a city that promises unforgettable experiences, from its majestic palaces to its vibrant cultural heritage. With Motoshare, you can make the most of your trip by having the perfect vehicle at your disposal.

Don’t wait—start your journey now:

• For car rentals in Vadodara
• For bike rentals in Vadodara

Explore Vadodara the way it’s meant to be explored—on your terms, with Motoshare!

The post Effortless Travel in Vadodara with Motoshare’s Online Booking Platform appeared first on Artificial Intelligence.

For the restaurant industry, 2020 was a year marked by rapid innovation and adaptation amid the COVID-19 pandemic. Throughout the year, restrictions tightened and loosened across the country, forcing brands to adapt their business models at an unprecedented pace in order to survive. Now, in 2021, the industry’s focus is turned less toward adaptation and more toward improving operations and recapturing profits in the midst of supply issues, high third-party delivery fees, and staffing difficulties, which all strain the bottom line.

As a result, many chains have focused their energies on creating and maintaining online ordering platforms, introducing mobile applications, and incentivizing customers to return through customizable loyalty programs and targeted digital marketing campaigns. However, restaurants are now managing more channels than ever before, requiring a presence in drive thru, carry out, delivery, and online ordering, as well as in newly reopened dining rooms, making it even more challenging to manage these programs. While restaurants have turned to technology to streamline these initiatives, finding all the resources they need in one platform has traditionally been difficult, especially for small to mid-sized brands.

Just ask Justin Egan, chief marketing officer and co-founder at Wing It On!, a growing North Carolina-based quick-service chicken brand with eight brick-and-mortar locations across six states. Egan says that while the leaders of the brand—which first opened in 2011 and began franchising in 2014—knew its customer base was adopting online and mobile ordering, that technology was out of reach for smaller brands without deep enough pockets to develop it for themselves or to hire a vendor to build it. When they were finally able to introduce a mobile app in 2018, it was only able to list hours and locations and provide guests with photos of dishes.

“We decided we needed something more user friendly that could provide a branded Wing It On! experience and the functionality to not only manage digital orders, but that could also help us connect with our customers and drive digital revenue without requiring too much manual maintenance,” Egan says.

That’s when Wing It On! turned to Incentivio, an all-in-one digital platform that helps restaurants manage their marketing, branded mobile apps and web ordering, loyalty programs, delivery, gift cards, curbside pickup, and customer database. 

“Introducing online and mobile ordering that was fully integrated with our POS was really what drew us initially to Incentivio,” Egan says. “The platform is very robust and offers tons of great features for restaurants to leverage that would typically require integrating, paying for, and managing multiple platforms, databases, and vendors. We can manage our mobile and online ordering, loyalty programs, push notifications, rewards in one platform that also provides a machine learning driven marketing engine. It was a game changer for us at so many levels”

Rich Long, director of implementation at Incentivio, was previously an Incentivio customer in his role as director of IT for several different restaurants across the quick-service, fast-casual, and full-service segments. He says he knows firsthand just how cumbersome managing so many disparate systems can be.

“When you maintain multiple systems for your data, online and mobile ordering, loyalty programs, and more, your entire ecosystem is ripe for errors,” Long says. “This is often because if you change something in one place—say an item you offer in your online ordering system—you might forget to change it everywhere else. Having all these systems in one platform and a single database allows you to reduce errors and save both time and money, while also creating a single source of information so all your reports are integrated.” 

Egan says Incentivio makes managing the company’s loyalty program frictionless. Guests earn incentives based on their orders and actions, like referring friends, and each time the brand launches a new LTO, Incentivio makes it easy for Wing It On! to announce new offerings through both push notifications and email.  

“Incentivio has allowed us to create a digital experience that’s both convenient and easy for our fans and simple to manage for our company; our franchisees are often astonished at how much we can offer with this one system,” Egan says. “In fact, we’ve even seen candidates who passed on franchising with us in the past come back and sign franchise agreements with us because we use Incentivio.”

Not only has the system helped save Wing It On! time and generate sales through the built-in marketing tools, but the chain is now also using Incentivio’s new autonomous recommendation engine. This new suggestive upsell tool uses past data to understand which sides, desserts, and beverages are commonly ordered with center-of-the plate items and which items a guest already has in his or her cart to make smart recommendations that help restaurants generate incremental revenue and increase average check sizes. “Simply by turning this feature on, we’ve seen a 4 percent increase in digital revenue driven autonomously through the upsells engine—we didn’t have to lift a finger to start seeing results,” Egan says. 

“In 2020, the restaurant industry had to become lean,” Long says. “You can no longer have an IT department of 25 and an operations team three layers deep, so the more restaurants can simplify the better. By bringing all of these tools into one easy-to-use yet powerful platform, restaurants of all sizes have access to the technology they need to become more efficient, save time, and drive sales without a multi-million dollar IT budget.”

Long adds that just from the new upsell recommendations engine alone, some of Incentivio’s customers are generating more in automated incremental revenue each month that the cost of the entire Incentivio platform.  

“As an emerging brand that was actually thriving during the pandemic due to our digital presence, it was difficult to find technology partners that were also thriving and innovating,” Egan says. “We felt well positioned to have Incentivio as a partner during this time. They never dropped the ball, and that really stresses the importance of choosing the right vendor partners to grow with you through the thickest of times. Wing It On!’s future is bright, and we’re on trajectory to have 100 stores in the pipeline by 2024. We know Incentivio is able to quickly scale with us, and we’re excited to continue our relationship.”

The post A Machine Learning Powered Guest Experience Platform is Helping This Emerging Brand Grow appeared first on Artificial Intelligence.

Recently, Apple researchers, including C. V. Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey, have developed Trinity, a no-code AI platform for complex spatial datasets. 

The platform enables machine learning researchers and non-technical geospatial specialists to experiment with domain-specific signals and datasets to solve various challenges. It tailors complex Spatio-temporal datasets to fit standard deep learning models–in this case, Convolutional Neural Networks (CNNs), and formulate disparate problems in a standard way, eg. semantic segmentation.

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Recently, Apple researchers, including C. V. Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey, have developed Trinity, a no-code AI platform for complex spatial datasets. 

The platform enables machine learning researchers and non-technical geospatial specialists to experiment with domain-specific signals and datasets to solve various challenges. It tailors complex Spatio-temporal datasets to fit standard deep learning models–in this case, Convolutional Neural Networks (CNNs), and formulate disparate problems in a standard way, eg. semantic segmentation.

Fill the Survey: Utilizing Behavioural Science to Analyze Customer Behaviour

“It creates a shared vocabulary leading to better collaboration among domain experts, machine learning researchers, data scientists, and engineers. Currently, the focus is on semantic segmentation, but it is easily extendable to other techniques such as classification, regression, and instance segmentation,” as per the paper.

Challenges

With the increase in smart devices, a high volume of data containing geo-referenced information is generated and captured. ML techniques have now entered the geospatial domain, including hyperspectral image analysis, high-resolution satellite image interpretation. However, deploying such solutions is still limited due to specific challenges, such as:

• Processing large volumes of Spatio-temporal information and applying ML solutions involves specialised skills and hence has a high barrier of entry, preventing non-technical domain specialists from solving problems on their own.
• The solution differs as data from residential areas will be very different from commercial ones, giving rise to non-standard preprocessing, post-processing, model deployment, and maintenance workflows.
• Engineers process data while scientists run experiments for different problems and involve a lot of back and forth. This hampers the ability to collaborate.

Trinity tackle these challenges by: 

• Bringing information in disparate Spatio-temporal datasets to a standard format by applying complex data transformations upstream. 
• Standardising the technique of solving disparate-looking problems to avoid heterogeneous solutions.
• Providing an easy-to-use code-free environment for rapid experimentation, thereby lowering the bar for entry.

It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment.

The post Now Apple Introduces A No-Code AI Platform appeared first on Artificial Intelligence.

“AI” is becoming a construct that has been the subject of increasing attention in technology, media, business, industry, government and civil life during recent years.

Today’s AI is the subject of controversy. You might have heard about narrow/weak, general/strong/human level and super artificial intelligence, or about machine learning, deep learning, reinforced learning, supervised and unsupervised learning, neural networks, Bayesian networks, NLP, and a whole lot of other confusing terms, all dubbed as AI techniques.

Many of the rules and logic-based systems that were previously considered Artificial Intelligence are no longer AI. In contrast, systems that analyze and find patterns in data are dubbed as machine learning, widely promoted as the dominant form of AI.

What is Wrong with Today’s AI, Its Chips and Platforms?

All the confusion comes from an anthropomorphic Artificial Intelligence, AAI, the simulation of the human brain using artificial neural networks, as if they substitute for the biological neural networks in our brains. A neural network is made up of a bunch of neural nodes (functional units) which work together, and can be called upon to execute a model.

Thus, the main purpose in 2021 is to provide a conceptual framework to define Machine Intelligence and Learning. And the first step to create MI is to understand its nature or concept against main research questions (why, what, who, when, where, how).

So, describe AI to people as an AAI or augmented intelligence or advanced statistics, not artificial intelligence or machine intelligence.

Now, re the levels of AAI applications, tools, and platforms?

Lets focus only on “AAI chips”, forming the brain of an AAI System, replacing CPUs and GPUs, and where most progress has to be achieved.

While typically GPUs are better than CPUs when it comes to AI processing, they usually fail, being specialized in computer graphics and image processing, not neural networks.

The AAI industry needs specialised processors to enable efficient processing of AAI applications, modelling and inference. As a result, chip designers are now working to create specialized processing units.

These come under many names, such as NPU, TPU, DPU, SPU etc., but a catchall term can be the AAI processing unit (AAI PU), forming the brain of an AAI System on a chip (SoC).

It is also added with 1. the neural processing unit or the matrix multiplication engine where the core operations of an AAI SoC are carried out; 2. Controller processors, based on RISC-V, ARM, or custom-logic instruction set architectures (ISA) to control and communicate with all the other blocks and the external processor; 3. SRAM; 4. I/O; 5. the interconnect fabric between the processors (AAI PU, controllers) and all the other modules on the SoC.

The AAI PU was created to execute ML algorithms, typically by operating on predictive models such as artificial neural networks. They are usually classified as either training or inference generally performed independently.

AAI PUs are generally required for the following:

• Accelerate the computation of ML tasks by several folds (nearly 10K times) as compared to GPUs
• Consume low power and improve resource utilization for ML tasks as compared to GPUs and CPUs

Unlike CPUs and GPUs, the design of single-action AAI SoC is far from mature.

Specialized AI chips deal with specialized ANNs, and are designed to do two things with them: task-designed training and inference, only for facial recognition, gesture recognition, natural language processing, image searching, spam filtering, etc.

In all, there are {Cloud, Edge, Inference, Training} chips for AAI models of specific tasks. Examples of Cloud + Training chips include NVIDIA’s DGX-2 system, which totals 2 petaFLOPS of processing power, made up of 16 NVIDIA V100 Tensor Core GPUs, or Intel Habana’s Gaudi chip or Facebook photos or Google translate.

Sample chips here include Qualcomm’s Cloud AI 100, which are large chips used for AAI in massive cloud datacentres. Another example is Alibaba’s Huanguang 800, or Graphcore’s Colossus MK2 GC200 IPU.

Now (Cloud + Inference) chips were used to train Facebook’s photos or Google Translate, to process the data you input using the models these companies created. Other examples include AAI chatbots or most AAI-powered services run by large technology companies. Here is also Qualcomm’s Cloud AI 100, which are large chips used for AAI in massive cloud datacentres, Alibaba’s Huanguang 800, or Graphcore’s Colossus MK2 GC200 IPU.

(Edge + Inference) on-device chips examples include Kneron’s own chips, including the KL520 and recently launched KL720 chip, which are lower-power, cost-efficient chips designed for on-device use; Intel Movidius and Google’s Coral TPU.

All of these different types of chips, training or inference, and their different implementations, models, and use cases are expected to develop the AAI of Things (AAIoT) future.

How to Make a True Artificial Intelligence Platform

In order to create a platform neutral software operating with world’s data/information/content which could run/display properly on any type of computer, cell phone, device or technology platform, the following are required:

• Operating Systems.
• Computing/Hardware/Cloud Platforms.
• Database Platforms.
• Storage Platforms.
• Application Platforms.
• Mobile Platforms.
• Web Platforms.
• Content Management Systems.

The AI programming language should act as both the general programming language and computing platform. Its applications could be launched on any operating system and hardware, from mobile-based operating systems, as Linux or Android, to hardware-based platforms, from game consoles to supercomputers or quantum machines.

The post HOW TO CREATE AN ARTIFICIAL INTELLIGENCE GENERAL TECHNOLOGY PLATFORM appeared first on Artificial Intelligence.

Over the last decade, data science has been rapidly progressing both as a technology and as a discipline. Best practices have been created by the leading businesses and it is now becoming part of the operational core for organizations. However, there is a need for a next step for product evolution in data science platform that supports and provides both business users an integrated solution for managing, building, and optimizing predictive models.

Nowadays, data science platform is the most talked about topic in data science meet-ups, conferences, and top publications. According to a Research Dive analyst review, the concept of data science platform is not novel in the big data space but the need of data science platform in business is still unknown to many.

Need for a Data Science Platform

 1)    To Enable Better Teamwork with Data Scientists

If the data scientists are solving the same problem in several ways, the productivity will decrease as it won’t deliver effectual value to the organization. One of the best solutions to ensure effective teamwork with data scientists is to provide them with a centralized flexible platform and the required set of tools to work upon. By using a data science platform, it ensures that all the contributions of the data scientists i.e. data models, data visualizations, and code libraries exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects, and share best practices to make data science easily scalable and less resource exhaustive.

2)    Help Minimalize Engineering Effort

With data science platforms, the data scientists get help in moving analytical models into production without any need of additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine then the data scientist will require the efforts of a software engineer for testing, refining and integrating the data model before the users start seeing the product recommendations on the basis of their behavior. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts.

3)    Help to Offload a Number of Low Value Tasks

The burden of data scientists is released with the help of data science platforms. The burden of low value tasks such as reproducing past results, configuring environments for non-technical users, running reports, and scheduling jobs is offloaded from data scientists.

4)    Facilitate Faster Research and Experimentation

Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on. Moreover, whenever there is a new hire in the data science team, the employee can quickly start working as it is easier to restore the work of the people who leave through a unified platform over various isolated tools.

The Market Overview

Currently, the global market for data science platform is progressing rapidly and is about to positively grow in the near future. According to the Research Dive report, the global data science platform market is projected to garner a revenue of $224.3 billion at a 31.1% CAGR from 2019 to 2026. This is majorly due to the growing adoption of analytical tools across the globe for learning the unobserved customer purchasing pattern. The key prominent players of the market are adopting several strategies such as product development along with many approaches such as collaborations and R&D activities to stand strong in the global market. The major players of the global data science market include Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc.

The post Data Science Platform Market: created an opportunity to transform in various sectors appeared first on Artificial Intelligence.

Fundstrat Digital Asset Consulting has produced another report updating its clients on BSV ecosystem progress. The report, released exclusively to investors last week, refers to BSV as “a Web 3.0 platform,” describes the concept of “Blockchain as a Service (BaaS)”, big data application potential, and gives some examples of the companies building on the network in different ways.

Similar to previous reports, Fundstrat (headed by well-known Wall Street strategist Thomas Lee) describes BSV’s “unique technological approach” as its most compelling aspect. Its large-scale data processing capabilities, combined with the security and verifiability of blockchain records, mean that developers are building real applications with real use cases. Industries seriously looking at the BSV network include supply chains, healthcare, payments, real estate, e-government services and more.

The emergence of BaaS infrastructure solutions will make enterprise adoption easier, the report says. Some of the projects it profiles are aimed at building solutions for others to come onboard without first spending years studying Bitcoin and blockchain history and the technical challenges of integrating their systems.

Fundstrat has published previous reports on BSV to its list of investor clients, in November 2019 and February and July 2020. Those reports also focused on BSV’s big data focus and “unique vision” compared to other blockchain networks, and highlighted the growing number of businesses building its infrastructure.

It says:

“The BSV and BTC networks are going after very different use cases. BSV is positioning itself as a Web 3.0 platform upon which Dapps and enterprise data applications are built that can store data directly on the main block chain layer (efficiently, for low fees) due to its unbounded block size cap. This approach has tradeoffs but is unique relative to other blockchains as it allows for certain types of big data applications that may not be well suited for other networks.”

The report includes a PowerPoint presentation that gives a general overview of the BSV network and its architecture. It details the functions of services like mAPI and MinerID for obtaining transaction fee information, and the relationships between miners (transaction processors), pools and ordinary users. It describes the structure of the BSV Metanet and provides statistics on BSV unit price, volumes, transaction counts, exchanges and average per-transaction fees (notably, these are US$0.0013 on BSV, $8.22 on Ethereum and $10.16 on BTC).

In targeting the “big data” enterprise market, BSV should serve the “Three Vs of Big Data” which are volume, velocity and variety. The report lists the benefits of users’ ability to own their own data and keep it both private and secure, noting the massive costs to organizations in the event of a data breach.

Companies named in the report include UNISOT, Veridat, nChain, TAAL, Fabriik, Planaria, MetaStreme, Mattercloud, FYX, BitBoss, Haste, Transmira, Twetch and Relica, EHR Data, and wallets HandCash, RelayX, DotWallet and Money Button. These companies cover a wide variety of big data use scenarios, showing BSV’s versatility and potential business models.

Fundstrat does warn that becoming involved with BSV carries risk due to the criticism it often receives from other sectors of the blockchain industry, saying “We recognize that there is a subset of the crypto community that does not have a favorable view of certain BSV supporters and the BSV coin or anyone who associates with it as an extension.”

We should note that much of that criticism comes from coordinated attacks on BSV from those who stand to lose the most commercially should BSV’s superior technology continue to prove itself over existing platforms. ETH routinely fails to scale to perform contract functions at both technical and economic levels, while BTC’s deliberately crippled capacity make its transaction fees prohibitively high to use as a payments network.

However, the report does mention that the blockchain industry is maturing and “maximalism fading.” While BSV would need to prove it can overcome this polarization and move past its “early reputational criticisms” in order to succeed, BSV’s focus on enterprise tier data applications and the BaaS infrastructure could convince more outsiders to take the plunge. Building a healthy user community was critical to achieving success, it concluded.

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Dataminr, a New York-based company specializing in AI that provides real-time information to customers, today announced it has raised $475 million at a post-money valuation of $4.1 billion. The company says the capital will be used to accelerate the growth of its corporate business line, which spans physical safety and security, reputation risk and crisis management, business intelligence, and cyber threat detection. Dataminr also plans to make investments in internationalization, expanding its private and public sector sales footprint across the Europe and Asia Pacific regions.

Data analytics is the science of analyzing raw data to extract meaningful insights. Market Research Future predicts that the global data analytics market will be valued at over $132 billion by 2026. A range of organizations can use data to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. Businesses that use big data increase their profits by an average of 8%, according to a survey conducted by BARC research.

Dataminr, which was founded in 2009 by Yale graduates Ted Bailey, Sam Hendel, and Jeff Kinsey, offers an information discovery platform that detects patterns of emerging events and information from public data signals. While in college, Bailey studied the impact of real-time data on society and was specifically interested in how technologies change the way humans become aware of world events. In 2008, Bailey became fascinated by the new wave of platforms like YouTube and Twitter and realized these tools gave people the ability to capture events as they’re happening. Bailey also realized these platforms might be able to fill in information gaps during events if scaled.

Today Dataminr claims to perform trillions of daily computations across billions of public data inputs in more than 150 languages, drawing on text, images, videos, logs from sensors, and multimodal combinations of these formats from over 100,000 sources. These sources span blogs, global and regional social media sites, web forums, local digital media, radio and audio transmissions, the deep and dark web, cyber signals, and internet of things devices.

Dataminr develops products targeting businesses, the public sector, and newsrooms, all of which use a combination of AI to parse the public data it regularly analyzes. Among the techniques the company employs are natural language processing, computer vision, audio processing and classification, and anomaly detection, all of which help surface “high-impact” events and emerging risks in real time. For example, one of the world’s largest airlines uses the platform to detect events that require adjustments in flight schedules, Dataminr says.

“Dataminr has invested highly in deep learning in the last few years, which has enabled the company to pioneer new AI signal detection fields, like multimodal fusion AI, which synthesizes real-time inputs in different data formats into multi-variable event detection models,” a spokesperson told VentureBeat via email. “Dataminr can take advantage of its now over 11-year proprietary data archive, which holds the patterns of how all events were recorded in digital data and serves as the essential foundation upon which Dataminr can continue to train and update its AI models. On average, Dataminr signals on breaking events are delivered to our clients nearly four hours ahead of a wire service like the Associated Press.”

Dataminr first came into the public eye in 2011, when it issued an alert that Osama bin Laden had been killed 23 minutes before major news organizations did. In 2019, Dataminr claimed to have detected the first signs of the COVID-19 outbreak in Wuhan on local Chinese social media platforms like Weibo and went on to identify clusters indicating future spikes in 14 U.S. states.

But Dataminr has often flirted with controversy. In 2020, the Intercept released a report showing that police departments used the company’s services for surveillance during the George Floyd protests, including accessing social media posts about protest locations and actions. The piece noted that the monitoring seemed at odds with claims from Dataminr that the company would neither engage in nor facilitate surveillance. This followed a string of bad press in 2016, when Twitter cut off geospatial data access for police intelligence centers.

Dataminr’s public image problems haven’t impacted business, though, with the roughly 650-person company reporting a doubling in revenue three years in a row from its corporate enterprise business line. The company’s clients include CNN, USA Today, the United Nations, Airbus, Shell, and the New York City Office of Emergency Management, among others.

“Large corporate clients are always discovering new use cases for our signals as they adopt Dataminr’s platform more broadly across their organization,” Bailey told VentureBeat via email. “As you can imagine, knowing about what is happening in the world faster than ever before possible, and at a scope unparalleled in human history, has a wide range of multi-dimensional use cases for corporate enterprises.”

Existing investors Valor Equity Partners, Morgan Stanley Tactical Value Fund, MSD Capital, The Pritzker Organization, DNS Capital, Moore Capital Management, Eldridge, ReInvent Capital, and Arrow Mark Partners participated in Dataminr’s latest funding round. It brings the company’s total raised to date to over $1.05 billion, following a $391.6 million series E round in June 2018.

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As known to many, Google has recently released Model Search which is an open-source platform. This caters to developing efficient and best machine learning models automatically. Rather than focusing on a particular domain, Model Search is domain agnostic and flexible beyond imagination. Well, not just that. To our surprise, it is even capable of finding just the right architecture. With this, it will best fit a given dataset and the associated problem. At the same time, it is mastered enough to minimize the time that goes behind in coding, the effort as well as the resources that are put in.

Model Search is built on Tensorflow. It is flexible to the extent that it can run either on a single machine or in a distributed setting. This feature does set it apart from the rest, without any doubt. It is equipped with multiple trainers, a search algorithm, a transfer learning algorithm and a database as well that aims at storing the various evaluated models.

The Architecture of Model Search

Talking about the architecture of Model Search, it is based on four foundational components: They are –

1. Model Trainers:As evident as it can get, these components cater to training and evaluating the various models asynchronously.
2. Search Algorithms: With this component of a search algorithm, it is possible to select the best trained architectures. It doesn’t end there. There’s also an option for the user to add some “mutations” to it which can be sent to the trainers for further evaluation.
3. Transfer Learning Algorithm:Model Search boasts of using transfer learning techniques such as knowledge distillation. This further brings in an advantage of reusing knowledge across different experiments. Model Search enables this using two ways – knowledge distillation or weight sharing. The former allows improving the accuracy by adding a loss term that matches the predictions of the high-performing models. On the other hand, the latter bootstraps some of the network’s parameters from previously trained candidates.
4. Model Database:This is where the results of the experiments can be persisted in such a way that it can be reused on different cycles.

What Makes Model Search So Unique?

• Now, that one aspect which makes people look forward to Model Search is its ability to run training and evaluation experiments for AI models in an adaptive and asynchronous fashion. This ability paves the way for the trainers to share the knowledge that they’ve gained from their experiments. The working is such that at the beginning of every cycle, the search algorithm closely monitors all the completed trials. After this, what follows is deciding what to try next. This platform makes use of beam search while deciding what is to be tried next. The next step that follows is to invoke mutation and assign the resulting model back to a trainer.
• When the users go for a Model Search run, they are in a position to compare the many models found during the search. Well, there’s more to this. The platform also allows the users to create their own search space. With this, it is possible for them to customize the architectural elements in their models.
• The researchers have also claimed that Model Search improves upon production models with minimal iterations.

All in all, The Model Search Code aims to provide the researchers with a flexible, domain-agnostic framework to develop the most efficient and best machine learning model. Also, the framework is so powerful that it can build models with state-of-the-art performance. It also has the capability to deal with well-known problems when provided with a search space composed of standard building blocks.

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