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How Much Does It Cost to Develop a Fantasy Sports App?

According to a recent report, the market revenue of online sports is expected to reach $2174.8 million by the end of 2023. This market research […]

The post How Much Does It Cost to Develop a Fantasy Sports App? appeared first on Quytech Blog.

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According to a recent report, the market revenue of online sports is expected to reach $2174.8 million by the end of 2023.

This market research clearly depicts the growing craze and demand for e-sports, mainly fantasy sports, all over the world. Fantasy sports are online sports in which players build a virtual team with real players that are physically playing that sport somewhere.

Didn’t get? Well, let’s take an example- By using a fantasy sports app for cricket, users can make their own team of virtual players who are actually playing the match at some stadium. The loss or the victory of the user will depend on the players’ or team’s real performance on the ground.

Keeping in mind the popularity of these sports app, businesses are investing heavily in it. So, if you too want to jump into this pool with an exciting and engaging fantasy sports app, then this article is exclusively for you. Here, we have provided key features and the cost of development of this sports app.

Features of a Fantasy Sports App

You must be having a fair idea of a fantasy sports app by now. So, we are mentioning below the key features of this app for Admin and users or participants:

Admin

  • Admin login or sign in
  • Manage results and points
  • Manage payment and transactions
  • Team management
  • Manage games, contests, leagues, and players
  • Manage notifications and requests

Users or Participants

  • Sign up or registration
  • Profile settings
  • Home screen
  • Contests
  • Live score
  • Join contest

Apart from these, a good fantasy sports app offers its users the ability to socialize, have smart backend, provides private group games as well as achievement badges, and features live game feeds, push notification, real-time analytics, GPS location tracking, multiple payment modes, and chatbot API integration.

Cost of Fantasy Sports App Development

While considering how to make a fantasy sports app, the first thing that comes to mind is the cost of its development. Whether you choose a Fantasy Sports App development company or approach remote game app developers for your project, it is good to have an idea of the factors that determines the cost of a fantasy sports application.

Platform

The platform you want to build your fantasy sports app on is one of the important factors of the development cost. Choosing from native or hybrid and selecting the platforms where the app will run are the two things you should be clear of before handing over your project to the development company or any developer.

Well, if you have never been into this development business, then you can consult the support team of the company to get an idea of which platform will suit to sports app you want to develop.

Front-end and Back-end Development

An app has both front-end and back-end to be developed, and choosing the right technology for its development is a crucial decision. It also contributes a lot while calculating the total cost of the development.

Special Requirements

Providing various features in the app for saving time and efforts and delivering the ultimate experience to users, the developers might need to use third-party libraries during the development. This might increase or decrease the cost of development.

Location and Size of the Development Team

The complexity of the fantasy app and the location of your development company contribute to the total cost of the development. So, it is advised to take both these factors into account.

After you decide these factors for your fantasy sports application, you can reach out to any trustworthy fantasy sports app development company to know the exact figures.

How to Make Money Through a Fantasy Sports App?

Here is how a fantasy sports application can help you make a fortune:

  • Advertising
  • In-app purchases
  • Subscriptions and memberships
  • Sponsorships
  • Affiliate networking

Conclusion

With so many fantasy sports applications already in the market, the best way to stand out is to find out the gaps and provide users with something different and entertaining. For this, you need to contact a reliable Mobile app Development Company that provides a good value for your money.

hire game developer

Source: https://www.quytech.com/blog/key-features-and-development-cost-of-a-fantasy-sports-app/

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

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A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

AI

Google AI researchers want to teach robots tasks through self-supervised reverse engineering

Published

on


A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However, understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed]. Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

Google TRASS robot

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can teach the agent to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach a state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control so that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem of configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it had seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/3Rd18kkyUUc/

Continue Reading

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