Artificial Intelligence (AI) market is posing to become billions of dollar industry in next few years, as global spending by nations on AI is likely to touch around $35.8 billion in 2029 which reports a growth of 44% over the amount spent in year 2018.
Such, impressive growth shows, AI holds huge potential to attract big organizations as well small enterprises attracting them to implement AI-enabled services for better growth in the business. However, working with AI you need immense amount of meticulous data to train the model so that it can give the precise results.
Actually, to train an AI or ML model a high-quality training data is required, which is a challenging task for AI developers or machine learning engineers. As, to get the human like complex decisions from machines you need enormous volumes of accurately labeled and annotated training data through images or videos.
With the growing AI demand, data science team are under pressure to complete the projects but acquiring the training data at a large scale is the real challenge they are facing right now.
Why Do Enterprises Face Data Issue for AI Strategy?
As per the research by Dimensional Research and Aiegion survey, enterprise machine learning is just beginning, machine learning engineers or data scientist team size is smaller and the expertise of growing data science is not yet compatible to matured ML projects expertize.
And acquiring the training data is the biggest challenge for the success of an AI project. As per the survey, 96% of the AI projects fail or not started due to lack of training data technology that leads to the inability to train the ML algorithms resulting failure of the project.
Half of the AI Projects Never Get Deployed
Nowadays, big organizations or enterprises having more than 100,000 employees are more keen to implement AI strategy into their business model – but only 50% of such enterprises currently have one. The survey reinforces that AI is at nascent in the enterprise, as 70% of them firstly invested in AI/ML projects in the last 24 months.
While on the other hand, over half of the enterprises report they have undertaken fewer than four AI and ML projects. And only half of the enterprises have released AI/ML projects into the development to build a fully-functional model.
And as per the survey research only, less than two-thirds of them indicated that their ML project reached the completion point that is being trained on labeled training data sets which are relatively at the initial stage in the ML project life cycle. And more revealing immaturity of ML in the enterprise, is that why half of the projects never deployed.
Survey Statistic Why AI/ML Projects Fail:
- 78% of AI/ML Projects Shut ate some stage Before Deployment
- 81% Admit the process of training AI with data is more difficult than they expected
- 76% struggle by attempting to label or annotate the training data on their own.
- 63% try to build their own labeling and annotation automation technology.
And as per the research, around 40% of failed projects reportedly stalled during training data-intensive phases like training data preparation, algorithms training model validation, scoring and post-deployment enhancement.
Top Reasons for AI Projects Failure:
- Lack of Expertise (55%)
- Unexpected Complications (55%)
- Training Data Problems (36%)
- Lack of Model Certainty (29%)
- Deficient Budget (26%), and
- Lack of Efficient Staff (23%)
As already bespeak, around two-thirds report that ML projects not able to progressed beyond proof of concept and algorithms development to the phase of training data. Mostly this phase is not favorable for such developments, as 80% report that training the algorithms is more challenging than the AI engineers have expected.
Reasons Why Training Algorithms Data is Challenging:
- Not enough data
- Data not in a usable form
- Bias or errors in the data
- Don’t have the tools to label the data
- Don’t have the people to label data
Nevertheless, less than 4% have reported that training data has presented without any problems. Almost three-quarters of the AI engineers indicated that they try to label and annotate training data on their own. While around 40% suggested they rely wholly or partially on off-the-shelf, pre-labeled data to train their AI model.
Such issues, lead to 70% companies utilizing external services for their AI or ML projects with most of them focusing on data collection, labeling and annotations. As AI and ML engineers are rare to find and also expensive, the enterprise should find out external solution service providers for critical activities like data labeling and model scoring. This evidence is enough to outsource data annotation for more improved outcomes.
Enterprises designate a strategic value to their machine learning initiatives and expect AI and ML shall improve their businesses aspects and would be also disruptive in their sectors.
However, AI and ML projects are still at an early stage of development at enterprises. And data science and AI engineer teams are relatively small and experienced which affects the efficiency and outcome of these projects.
What AI Techniques Are Used In Chatbots: Explained with Examples
Chatbots are automated running application that can interact and communicate with humans in a common language through texts or voice-based audio conversation. Basically, chatbots work in AI-based technology, that can mimic like humans and help them in solving their queries pertaining to a particular problem on the platform.
Its seems interesting, the machine-driven application is solving human queries, but how these chatbots are developed and what kind of AI techniques are used in Chatbots development. You will get the answer of this question with examples and few most active and successful chatbot or similar applications used at large scale globally.
How AI Techniques Used in Chatbots?
To create such chatbots, a machine learning algorithm is used to learn the human language that is mainly used during such conversations. And Natural language processing (NLP) or natural language understanding (NLU) is used to put artificial intelligence in chatbots that allows computers to understand humans how they talk in normal language.
Actually, AI techniques used in chatbots through NLP which is the process of integrating the human spoken natural language used while communicating on a particular topic. When a chatbot is developed for a particular industry, sector or company, the keywords and statements containing the relevant words are taken into account to process through NLP.
For example, for the ecommerce industry, chatbot training data should contain the normal queries that come while placing the online order, making payments or about the delivery of products and exchange or return related queries. Once such data gathered, it is annotated with NLP annotations services to make the important words understandable to machines and learn from such communication and respond accordingly.
AI Technology in Chatbots
A virtual assistance is also one of the best examples, works like chatbots that answer the common queries of humans through voice commands. Google Assistant, Siri, and Amazon Alexa are the well-known virtual assistance devices and services offered by the tech giants using the same technology that is used in chatbot development.
Basically Chatbot is not an artificial intelligence-enabled applications, instead, it more based on machine learning where developers use the huge amount of chatbot machine learning training data and integrate the same with a right algorithm for the best response.
Machine Learning in Chatbot
Use of AI in chatbots is more or less depending on the developers’ feasibility and requirements for the particular field. But chatbots can be developed with machine learning that only requires a huge amount of training data sets to train the model.
The data contains the set of similar questions with the most relevant answers given while solving the customer’s problems. Chatbot learns what are the best answers given to pertaining questions or what actions should be taken if any irrelevant question comes during the conversation with humans.
However, dialog selection is a prediction problem, and the use of a heuristic program to identify the most suitable response template may include simple algorithms such as keywords matching or more complex processing with automatic learning or deep learning.
But with the help of more qualitative and quantitative training data and NLP integration, a chatbot can be developed for any industry or company as per the business operations.
These ChatBots are capable to provide a highly integrated online platform for varied customers to solve their queries instantly. And in future no doubt it will become a more powerful tool for businesses to provide personalized assistance to their customers improving the overall business-customer relationships with better business performance.
How AI Can Help In Agriculture: Five Applications and Use Cases
Artificial Intelligence (AI) is expanding its footprints at the ground level making a significant impact in the world’s most vital sector – Agriculture. After healthcare, automotive, manufacturing and finance sector now artificial intelligence in agriculture is providing cutting-edge technology for harvesting with better productivity and crop yield.
The Agriculture sector is the foundation of the world’s economy and with the increasing population, the world will need to produce 50% more food by 2050. AI-enabled technologies can help farmers get more from the land while using resources more sustainably. Here, we’ll learn how AI can be used in agriculture and its applications into farming.
AI Applications with Use Cases in Agriculture and Farming
With the heavy investment in developing autonomous vehicles for various needs, the agriculture sector will be also getting benefits with self-driving or you can say driverless tractors. With more quality training data for agriculture, the farm sector is going to be revolutionized by the large scale use of autonomous tractors for performing multiple tasks.
Video: Autonomous Tractor at Work
These self-driving or driverless tractors are programmed to independently detect their ploughing position into the fields or decide the speed and avoid obstacles like irrigation objects, humans and animals while performing various tasks.
Similarly, AI companies are developing robots that can easily perform multiple tasks in the farming field. Such robotics machines are trained to control weeds and harvest the crops at a much faster pace with higher volume compare to humans.
Video: AI Robots in Agriculture
These robots are well-trained to assist for checking the quality of crop and detect unwanted plants or weeds with picking and packing of crops at the same time capable to fight with other challenges faced by the agricultural labour force.
Companies like Blue River Technology and Harvest CROO Robotics are making such robotics machines that can control unwanted crops or weeds and help farmers in picking or packing of crops with higher volumes.
Controlling Pest Infestations
Pests are one of the worst enemies of the farmers damaging the crops globally before it is harvested and stored for human consumption. Popular insects like locusts, grasshoppers, and other insects are eating the profits of farmers and gobbling the grains meant for humans. But now AI in farming gives growers a weapon against such bugs.
AI and data companies are helping farmers to get alert on his Smartphones about the grasshoppers likely to descend towards a particular farm. AI companies using the new satellite images against pictures of the same using historical data and AI algorithm detects that the insects had landed at another location and farmers use such information after confirmation and timely remove the costly pests from their fields.
Soil and Crops Health Monitoring
Continues deforestation and degradation of soil quality are becoming a big challenge for food producing countries. But now a German-based tech startup PEAT has developed a deep learning based application called Plantix that can identify the potential defects and nutrient deficiencies in the soil including plant pests and diseases.
This app is working on image recognition based technology and you can use you your smartphone to capture the plant’s image and detect the defects into the plants. You will also get soil restoration techniques with tips and other solutions on short videos on this app.
Similarly, Trace Genomics is another machine learning based company provides soil analysis services to farmers. Such apps help farmers to monitor the soil and crop’s health conditions and produce a healthy crop with a higher level of productivity.
SkySquirrel Technologies acquired by another similar company VineView brought drone-based aerial imaging solutions for monitoring crops health. A drone is used to make a round of capturing the data from the vineyards field and then all the data is transferred via a USB drive from the drone to a computer and analyzed by the experts.
The company uses the algorithms to analyze the captured images and provides a detailed report containing the current health of the vineyard, generally the condition of grapevine leaves as these plants are highly prone to grapevine diseases like molds and bacteria helping farmers to timely control using the pest control and other methods.
Precision Farming with Predictive Analytics
AI applications in agriculture expanded into doing the accurate and controlled farming through providing proper guidance to farmers about optimum planting, water management, crop rotation, timely harvesting, nutrient management and pest attacks.
Video: What is Precision Farming?
While using the machine learning algorithms in connection with images captured by satellites and drones, AI-enabled technologies predict weather conditions, analyze crop sustainability and evaluate farms for the presence of diseases or pests and poor plant nutrition on farms with data like temperature, precipitation, wind speed, and solar radiation.
AI in agriculture not only helping farmers to automate their farming but also shifting to precise cultivation for higher crop yield and better quality while using less resources.
Companies involved in improving the machine learning or AI-based products or services like training data for agriculture, drone and automated machine making will get technological advancement in future will provide the more useful applications to this sector helping the world deal with food production issues for the growing population.
What is Deepfake: Know Everything About this AI-based Technology
Technologies are not only born to provide the goodness to humans, as few people make misuse of it and try to create illegal things that can embarrass them or hurt their sentiments deeply. Deepfakes, an AI-backed technology, is one that comes into the limelight due to wrong reasons defamed many people in the last few years.
What is Deepfake Technology?
Deepfake is an AI-supported technique to create ultra-realistic fake videos of people by swapping their faces other person saying and doing things that they haven’t actually done. It includes images, videos and audios to combine with images and videos onto source images or videos. Porn videos having the mocked face of popular celebrities on the adult star are one of the well-known examples of deepfakes.
What Does Deepfake Mean?
Using the AI with high-powered computer a graphics processing unit (GPU) and time on their hands can create realistic fake videos is known as “Deepfakes”.
Deepfakes are not only limited to videos but the audio is also targeted to create such fake voice-based contents. Though, it is much more difficult to compare the face swapping but the voice of another person can be manipulated with any other person speaking something that is not originally said by him seems very original.
How Does Deepfake Technology Work?
Neural network based technology is used to make Deepfake videos. The deep learning process is used into deepfake creation that is kind of ultramodern application of neural net simulation to massive data sets. Primarily neural networks are capable to “learn” to perform tasks by considering, generally without being programmed with any task-specific rule.
Deepfakes use the human tendency through generative adversarial networks (GANs) in which two machine learning models are trained with the data sets to create fake videos at the same time also detect fake videos. The forger ML model keeps continuing creating fake videos until the second model fails to detect the forgery.
How Deepfake Videos are Created
The larger the quantity of training data sets it would be easier for a forger to create deepfakes that can be believed easily. Political celebrities and Hollywood’s popular actresses became the victims of Deepfake technology misrepresenting their personalities and pose a grave threat to women not prominent could have their reputations damaged by the depiction in involuntary deepfake pornography or revenge pornography.
What Software is Used to Make Deepfakes?
There are many software available in the market to create deepfakes. One of them is FakeApp that allows the normal person to make the deepfake content, though this website has been banned but still many apps are active in the market.
Samsung AI has recently developed a new artificial intelligence system that can generate a fake clip by feeding it as little as one photo. Usually requires big data sets of images in order to create a realistic forgery but here you don’t need a huge amount of images or videos to create deepfakes misusing the AI for humans.
Samsung has used this technology into famous Leonardo Da Vinci painting of Mona Lisa in which it has assigned a series of ‘facial landmarks’ to the portrait and then applied an algorithm that has access to metadata from a vast amount of image banks to form a different ‘movements’ of the Mona Lisa’s head that you can spot below.
Popular Deep Fake News
The most popular Deepfake news was a fake video of the former US president Barack Obama in which he was expressing a profanity-laced opinion on US president Donald Trump.
Similarly, a fake video of Steve Buscemi came into the market in which her face was superimposed onto Jennifer Lawrence while speaking at the Golden Globes award show.
Apart from this, many other celebrities also became the news headlines due to deepfakes porn videos of popular actresses gone viral making millions of people excited to watch such contents. However, knowing the truth all the leading porn sites have removed such fake videos from their websites that were embarrassing such celebrities.
Deepfake in Pornography
Actually, Deepfake is a major threat to popular celebrities by creating their deep fake porn videos. The images of such celebrities or popular actresses are freely available on the Internet at their social media pages or other entertainment sites.
Many times such videos are created as revenge porn, in which deepfake creator needs a bunch of photos of the victim from various sources to superimposed the same on the body of a porn star and raunchy videos posted on adults websites.
Female celebrities including Taylor Swift, Natalie Portman, Emma Watson, Gal Gadot, Michelle Obama, Daisy Ridley, Meghan Markle, Sophie Turner and Kate Middleton had become the victims of deepfake pornography.
How to Detect Deepfake Video?
Detecting deepfakes is not possible with the normal human eye but there are few activities and signs that you can spot deepfake videos. Quality of video, irregular blinking of eyes, inconsistent skin tone, unnatural movement of the body or facial expressions and unusual changes in the background suchlike lighting conditions or color etc. are the major clues you can detect the deepfakes.
Video: Why Detecting Deep Fake is Difficult?
However, AI-enabled applications can detect such loopholes or fake videos using the same level of technology that has been used to create such contents. While on the other hand, human-powered deepfake detection services are also offered by companies to check and find out the fake videos, audio and texts with better accuracy.
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