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Top 7 AI Trends In 2019
August 15, 2019 | Artificial Intelligence, Machine Learning, Natural Language Processing, Technology of Tomorrow | No Comments
Artificial Intelligence is a method for making a system, a computer-controlled robot. AI uses information science and algorithms to mechanize, advance and discover worth escaped from the human eye. Most of us are pondering about "what's next for AI in 2019 paving the way to 2020?" How about we explore the latest trends in AI in 2019.
AI-Enabled Chips
Companies over the globe are accommodating Artificial Intelligence in their frameworks however the procedure of cognification is a noteworthy concern they are confronting. Hypothetically, everything is getting more astute and cannier, yet the current PC chips are not good enough and are hindering the procedure.
In contrast to other programming technologies, AI vigorously depends on specific processors that supplement the CPU. Indeed, even the quickest and most progressive CPU may not be capable to improve the speed of training an AI model. The model would require additional equipment to perform scientific estimations for complex undertakings like identifying objects or items and facial recognition.
In 2019, Leading chip makers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will improve the execution speed of AI-based applications. Cutting edge applications from the social insurance and vehicle ventures will depend on these chips for conveying knowledge to end-users.
Augmented reality (AR) is one of the greatest innovation patterns at this moment, and it's just going to become greater as AR cell phones and different gadgets become increasingly available around the globe. The best examples could be Pokémon Go and Snapchat.
Objects generated from computers coexist together and communicate with this present reality in a solitary, vivid scene. This is made conceivable by melding information from numerous sensors such as cameras, gyroscopes, accelerometers, GPS, and so forth to shape a computerized portrayal of the world that can be overlaid over the physical one.
AR and AI are distinct advancements in the field of technology; however, they can be utilized together to make one of a kind encounters in 2019. Augmented reality (AR) and Artificial Intelligence (AI) advances are progressively relevant to organizations that desire to pick up a focused edge later on the work environment. In AR, a 3D portrayal of the world must be developed to enable computerized objects to exist close by physical ones. With companies such as Apple, Google, Facebook and so on offering devices and tools to make the advancement of AR-based applications simpler, 2019 will see an upsurge in the quantity of AR applications being discharged.
A neural network is an arrangement of equipment as well as programming designed after the activity of neurons in the human cerebrum. Neural networks – most commonly called artificial neural networks are an assortment of profound learning innovation, which likewise falls under the umbrella of AI.
Neural networks can adjust to evolving input; so, the system produces the most ideal outcome without expecting to overhaul the yield criteria. The idea of neural networks, which has its foundations in AI, is quickly picking up prominence in the improvement of exchanging frameworks. ANN emulate the human brain. The current neural network advances will be enhanced in 2019. This would empower AI to turn out to be progressively modern as better preparing strategies and system models are created. Areas of artificial intelligence where the neural network was successfully applied include Image Recognition, Natural Language Processing, Chatbots, Sentiment Analysis, and Real-time Transcription.
The convergence of AI and IoT
The most significant job AI will play in the business world is expanding client commitment, as indicated by an ongoing report issued by Microsoft. The Internet of Things is reshaping life as we probably are aware of it from the home to the workplace and past. IoT items award us expanded control over machines, lights, and door locks.
Organizational IoT applications would get higher exactness and expanded functionalities by the use of AI. In actuality, self-driving cars is certifiably not a commonsense plausibility without IoT working intimately with AI. The sensors utilized by a car to gather continuous information is empowered by the IoT.
Artificial intelligence and IoT will progressively combine at edge computing. Most Cloud-based models will be put at the edge layer. 2019 would see more instances of the intermingling of AI with IoT and AI with Blockchain. IoT is good to go to turn into the greatest driver of AI in the undertaking. Edge devices will be furnished with the unique AI chips dependent on FPGAs and ASICs.
Computer Vision is the procedure of systems and robots reacting to visual data sources — most normally pictures and recordings. To place it in a basic way, computer vision progresses the info (yield) steps by reading (revealing) data at a similar visual level as an individual and along these lines evacuating the requirement for interpretation into machine language (the other way around). Normally, computer vision methods have the potential for a more elevated amount of comprehension and application in the human world.
While computer vision systems have been around since the 1960s, it wasn't until recently that they grabbed the pace to turn out to be useful assets. Advancements in Machine Learning, just as the progressively skilled capacity and computational devices have empowered the ascent in the stock of Computer Vision techniques. What follows is also an explanation of how Artificial Intelligence is born. Computer vision, as a region of AI examines, has entered a far cry in a previous couple of years.
Facial recognition is a type of AI application that aides in recognizing an individual utilizing their digital picture or patterns of their facial highlights. A framework utilized to perform facial recognition utilizes biometrics to outline highlights from the photograph or video. It contrasts this data and a huge database of recorded countenances to find the right match. 2019 would see an expansion in the use of this innovation with higher exactness and dependability.
In spite of having a lot of negative press lately, facial recognition is viewed as the Artificial Intelligence applications future because of its gigantic prominence. It guarantees a gigantic development in 2019. The year 2019 will observe development in the utilization of facial recognition with greater unwavering quality and upgraded precision.
Open-Source AI
Open Source AI would be the following stage in the growth of AI. Most of the Cloud-based advancements that we use today have their beginning in open source ventures. Artificial intelligence is relied upon to pursue a similar direction as an ever-increasing number of organizations are taking a gander at a joint effort and information sharing.
Open Source AI would be the following stage in the advancement of AI. Numerous organizations would begin publicly releasing their AI stacks for structuring a more extensive encouraging group of people of AI communities. This would prompt the improvement of a definitive AI open source stack.
Numerous innovation specialists propose that the eventual fate of AI and ML is sure. It is the place where the world is headed. In 2019 and beyond these advancements are going to support as more organizations come to understand the advantages. However, the worries encompassing the dependability and cybersecurity will keep on being fervently discussed. The ML and AI trends for 2019 and beyond hold guarantees to enhance business development while definitely contracting the dangers.
8 Common Myths About Machine Learning
August 1, 2019 | Artificial Intelligence, Machine Learning | No Comments
Artificial Intelligence and the idea of it has always been around be it research or sci-fi movies. But the advances in AI wasn't drastic until recently. Guess what changed? The focus moved from vast AI to components of AI such as machine learning, natural language processing, and other technologies that make it possible.
Learning models which form the core of AI started being used extensively. This shift of focus to Machine Learning gave rise to various libraries and tools which make ML models easily accessible. Here are some common myths surrounding Machine Learning:
Machine Learning, Deep Learning, Artificial Intelligence are all the same
In a recent survey by TechTalks, it was discovered that more than 30% of the companies wrongly claim to use Advance Machine Learning models to improve their operations and automate the process. Most people use AI and ML synonymously. How different are AI, ML and Deep Learning?
Machine Learning is a branch of Artificial Intelligence which has learning algorithms powered by annotated data which learn through experiences. There are primarily two types of learning algorithms.
Supervised Learning algorithms draw patterns based on the input and output values of the datasets. It starts predicting the outputs from the training data sets with possible input and output values.
Unsupervised learning models look at all the data fed into the model and find out patterns in the data. It uses unstructured and unlabeled data sets.
Artificial Intelligence, on the other hand, is a very broad area of Computer Science, where robust engineering and technological advances are used to build systems that need minimal or no human intelligence. Everything from the auto-player in video games to predictive analytics used to forecast sales fall under the same roof using some Machine Learning algorithms
Deep Learning uses a set of ML algorithms to model abstraction in data sets with system architecture. It is an approach used to build and train neural networks.
All data is useful to train a Machine Learning model
Another common myth around Machine learning models is that all the data is useful to improve the outputs of the model. The raw data is never clean and representative of the outputs.
To train the Machine Learning models to learn the accurate outputs expected, data sets need to be labeled with relevance. Irrelevant data needs to be removed.
The accuracy of the model is directly correlated to the quality of the data sets. The quality of the trained data sets results in better accuracy rather than a huge amount of raw/unlabelled data.
Building an ML system is easy with unsupervised learning and 'Black Box Models'
The most business decision will require very specific evaluation, to make strategic data-driven decisions. Unsupervised and 'Black Box' models use algorithms randomly and highlight data patterns making it biased towards patterns which aren't relevant.
The usability and relevance of these patterns to the objective the business the focus is on are a lot less when these models are used. Black box systems do not reveal what patterns they have used to arrive at certain conclusions. Supervised or Reinforcement learning trained with curated, labeled data sets can surgically investigate the data and give us the desired outputs.
ML will replace people and kill jobs
The usual notion around any advanced technology is that it will replace people and make people jobless. According to Erik Brynjolfsson and Daniel Rock, with MIT, and TomMitchell of Carnegie Mellon University, ML will kill the automated or painfully redundant tasks, not jobs.
Humans will spend more time on decision making jobs rather than repetitive tasks which ML can take care of. The job market will see a significant reduction in repetitive job roles but the wave of ML, AI will create a new sector of jobs to handle the data, train it and derive outcomes based on the ML systems.
Machine Learning can only discover correlations between objects and not causal relationships
A common perception of Machine Learning is that it discovers easy correlations and not insightful outputs. Machine Learning used in conjunction with thematic roles and relationship models of NLP will provide rich insights. Contrary to common belief, ML can identify causal relationships. This is commonly used to try out different use cases and observing the consequences of the cases.
Machine learning can work without human intervention
Most decisions from the ML models will need human intelligence and intervention. For examples, an airlines company may adopt ML algorithms to get better insights and influence best ticket prices. Data sets are constantly updated and complex algorithms may be run on it.
But, to decide the price of a flight by the system itself has a lot of loopholes, the company will hire an analyst who will analyze the data and sets prices with the help of models and their analytical skills, not just relying on the model alone.
The reasoning behind the decision making is still a human intelligence one. Complete control should not be rested on models for optimal results.
Machine Learning is the same as Data mining
Data mining is a technique to examine databases and discover the properties of data sets. The reasons its often confused is because Data Analytics uses these data sets using data visualization techniques. Whereas, Machine Learning is a subfield which uses curated data sets to teach systems the desired outputs and make predictions.
There is similarity when unsupervised learning Ml models use datasets to draw insights from them, which is precisely what data mining does. Machine Learning can be used for data mining.
The common confusion between the two arises due to a new term being used extensively, Data Science. Most Data mining-focused professionals and companies are leaning towards using Data science and analytics now causing more confusion.
ML takes a few months to master and is simple
To be an efficient ML Engineer, a lot of experience and research is needed. Contrary to the hype, ML is more than importing existing libraries in languages and using Tensor Flow or Keras. These can be used with minimal training but takes an experienced hand to provide accuracy.
A lot of intense Machine Learning focussed products require intense research on topics and even coming up with approaches using methods that are in discussion at a university or research level. Already existing libraries solve very generic problems people are trying to solve and not really insightful data. A deeper understanding of algorithms is needed to create an accurate model with an improved f1(accuracy) score.
To sum up, there is an overlap of concepts and models in Machine Learning, Artificial Intelligence, Data Science and Deep Learning. However, the goal and science of the subfields vastly vary. To build completely automated AI systems, all the fields become crucial and play a distinct role.
Debunking 5 Common Misconceptions About AI Systems
July 30, 2019 | Artificial Intelligence, Technology of Tomorrow | 1 Comment
Ever wondered what your life would be without those perky machines lying around which sometimes/most times replaced a significant part of your daily routine? In Terminology fancied by Scientists, we call them AI (Artificial Intelligence,) and in plain layman or lazy man terms that is us, we fancy calling them machines and bots.
Let's define the exact meaning of AI in terms of science because I hate disappointing aspiring scientists out there who don't take puns lightly. For those that do, welcome to the fraternity of loose and lost minds. Let's get down to business, shall we?
Definition: Artificial Intelligence or machine intelligence, is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind such as "learning" and "problem-solving."
Isn't it evident I copied the above definition from Wikipedia? And did your natural intelligence decipher the meaning of the definition stated above?
Let me introduce you to the lazy man definition of Artificial Intelligence. Like all engineering scholars, I will take the absolute pleasure of dismantling the words and assembling it together again.
Artificial – Non-Human, something that can't breathe air or respond to a feeling.
Intelligence – the ability to display intellect, sound reasoning, judgment, and a ready wit.
Put the two words together and voila! Artificially intelligent machines are capable of displaying or mimicking human intellect, sound reasoning, and judgment towards it's surrounding.
Now that we got the definition of AI out of the way, look around you, what do you see? What's in your hands? Do you not spot a single electronic device or bots?
Things or machines work a lot differently in this era. You must be awestruck of the skyrocketing shiny monuments. The big bird moving 33,000ft above your head carrying humans from one country to another, hospitals treating the diseased and the ill with technology your mind can't fathom.
Fast cars, microwave and yes, we no longer communicate using crows or pigeons we have cell phones!
Don't be surprised if I reveal that these are the necessity and an extension to our lives. And no, we cannot live without them anymore.
Our purpose of life has changed drastically, growing crops and putting food on the table isn't what give us lines on the forehead. We built replacement models that take care of that too. We are living in a fast lane where technology, eventually, will slingshot us to the moon or another planet.
With such a drastic rise in AI and the current trend where all companies want a piece of it, there are some misconceptions about AI as well. With this blog, I try to debunk the misconceptions highlighting both the positive and negative aspects of artificial intelligence.
"If these machines are handling even the simplest of tasks, what are people going to do? Is it the destruction of jobs?"
Fret not. If there is technological advancement, there are always career opportunities as it is the human mind that does the 'thinking.' You are the master of your creation.
In fact, in 2020 there will be 2.3 million new jobs available thanks to AI, which results in less muscle power and more brainpower.
"Can Artificial Intelligence solve any/all problems?"
This question is debatable, while AI is designed to assist and make our jobs easier, it cannot save a human being from rubbing off cancers and illness.
Human intelligence hasn't discovered a way to program the bots to predict or diagnose illness proactively. One must remember, bots act on what is fed/programmed by humans.
"Is AI infallible?"
If you thought it was, then I have slightly bad news. Humans are in a common misconception assuming the machines are no less than perfection and display little to no mistake. The non-sentient systems are trained by us, data selected and curated by us, and human tendency is to make mistakes and learn from them.
Artificial Intelligence is just as good as the training data used, which is created by humans. Any mistake with the training data will reflect on the performance of the system and the technology will be compromised. Ensuring you use a high-quality training dataset is critical to the success of the AI system.
Speaking of data being compromised, during the 2016 presidential election campaign, we witnessed the information of US citizens being evaluated by gaining access to their social media accounts. To proactively block their social media feeds with ads that will prove to be of interest. Therefore, stealing away the votes from the opposition.
We call this "data/information manipulation." Sadly, the downside of Artificial Intelligence.
"AI must be expensive."
Well, implementing a fully automated system doesn't come easy and doesn't come cheap. But depending on the needs and goals of the organization, it may be entirely possible to adopt AI and get the desired results without breaking your treasure chest.
The key is for each business to figure out what they want and apply AI as needed, for their unique goals and company scale. If businesses can workout their scalability and incorporate the right Artificial intelligence, it can be economical in the long run.
"Will Artificial Intelligence be the end of humanity?"
We are a work in progress, standing at the foyer of technological advancements with a long way to go. But, much like the misconception about robots replacing humans in the workforce, the question is more of smoke in the mirror.
The AI in its current level is not fully capable of self-conscious and decision making. Don't let Star Trek, Iron Man and Terminator movies fool you into believing bots will lose their nuts (literally and hypothetically) and foreshadow the destruction of humanity. On the flip side, it is the natural disasters the bots are being designed to protect us from.
Oh, look what's in every body's hand, it's what we call a cell phone. A device primarily designed to communicate with people that are at a greater distance.
Communication takes place using microwaves, very different from sand waves. Look closely and you'll see people doing weird things using their fingers on the cell phone and a weird thing hanging from their ears going through to the same device. Yes, these devices are their partners for life.
Here we are, say Konnichiwa to the lady, don't touch her! She's just a hologram.
Welcome to the National Museum of Emerging Science and Innovation simply known as the Miraikan (future museum) where obsessiveness over technology has led us to build a museum for itself.
There's Asimo, the Honda robot and, what you're looking at isn't another piece of asteroid that struck earth years ago, it is Geo-Cosmos. A high-resolution globe displaying near real-time events of global weather patterns, ocean temperatures, and vegetation covering across geographic locations.
You must be contemplating why has mankind reached such level of advancement? Let's go back to the last question "Will AI be the end of humanity?"
The seismometer, a device that responds and records the ground motions, earthquake, and volcanic eruptions. There are a lot of countries that have lost far too many lives to even comprehend the tragic events of active earthquakes.
This device is a way to predict and bring citizens of Japan to safe grounds. Artificial Intelligence will not be the end of humanity, it can, in fact, be the opposite and could be an answer to humanity's biggest natural calamities and disasters.
The human mind is something to behold, from its complex neural nerves in the brain to the nerves connecting to every part of the body to achieve motor functions. To replicate or clone it using artificial chips and wires is nearly impossible in the current era but the determination we hold and our adamant nature drives us to dream, the dream of one day successfully cloning the human consciousness into nuts and bolts of a bot.
One day to look at the stars and send bots for space exploration. To look for a suitable second home in an event of space disasters that humans have no control over. And, why send bots into deep space and not humans to add a feather to the hat of achievement?
Simply because we breathe, we starve, and our very own nervous system advertently detects the brutal nature of space above the earth. In this case, Artificial Intelligence and robots are in fact helping humans explore the possibilities of life in outer space. Which is against the misconception that AI will be the end of humanity.
So, there we have it, all the major misconceptions about artificial intelligence and what the reality is. End of the day, it all comes down to how we incorporate artificial intelligence and what we use it for.
If used in the right way, there will be a revolution in the way humans work. Which makes it important for all of us to work on educating people about artificial intelligence and using it to make the world a better place.
Understanding the difference between AI, ML, NLP models.
July 26, 2019 | Artificial Intelligence, Machine Learning, Natural Language Processing, Technology of Tomorrow | No Comments
Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today's leading-edge technologies.
These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.
Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.
AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.
While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.
History of AI
The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine's ability to exhibit intelligence.
The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.
Google's Android app "Google Now", launched in the year 2012 was again an AI application. The most recent wonder of AI is "the Project Debater" from IBM. AI has currently reached a remarkable position
The areas of application of AI include
Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that's not the end of it.
What is Machine Learning?
One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…
Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.
History of Machine Learning
The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.
The most important feature of ML is "Self-Learning". The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). "The Nearest Neighbor" algorithm was written for pattern recognition.
Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.
Different types of machine learning approaches are:
Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.
Unsupervised Learning doesn't have a training data set but can be used to detect repetitive patterns and styles.
Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.
ML has several applications in various fields such as
Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions.
HealthCare: The use of different sensors and devices use data to access a patient's health status in real-time.
Financial Services: To get the key insights into financial data and to prevent financial frauds.
Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.
What is Natural Language Processing?
Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.
Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.
History of NLP
NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early '90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM's Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri's speech recognition in the field of NLP's research and development is booming.
Few Applications of NLP include
Sentiment Analysis – Majorly helps in monitoring Social Media
Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
Text Classification – Text classification is used to assign tags to text according to the content.
Grammar Correction – Used by software like MS-Word for spell-checking.
The term "Deep Learning" was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.
The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses "Supervised Learning" where a neural network is trained using "Unsupervised Learning".
Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several 'nodes' that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…
History of Deep Learning
The history of Deep Learning includes the introduction of "The Back-Propagation" algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML. Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997. In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks
Applications of Deep Learning are
Text and Character generation – Natural Language Generation.
Automatic Machine Translation – Automatic translation of text and images.
Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that's not the end of it.
Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.
Key Differences between AI, ML, and NLP
Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren't the same.
ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).
The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.
Understanding Training Data & How To Build High-Quality Training Datasets For AI/ML Models
July 18, 2019 | All About Data, Artificial Intelligence, Machine Learning | No Comments
We are living in one of the most exciting times, where faster processing power and new technological advancements in AI and ML are transcending the ways of the past. From conversational bots helping customers make purchases online to self-driving cars adding a new dimension of comfort and safety for commuters. While these technologies continue to grow and transform lives, what makes them so powerful is data.
Tons and tons of data.
Machine Learning systems, as the name suggests, are systems that are constantly learning from the data being consumed to produce accurate results.
If the right data is used, the system designed can find relations between entities, detect patterns, and make decisions. However, not all data or datasets used to build such models are treated equally.
Data for AI & ML models can be essentially classified into 5 categories: training dataset, testing dataset, validation dataset, holdout dataset, and cross-validation dataset. For the purpose of this article, we'll only be looking at training dataset and cover the following topics.
What is a training dataset?
Why are training datasets important?
How to build a training dataset?
Data Preprocessing
What Is Training Data
Training data also called training dataset or training set or learning set, is foundational to the way AI & ML technologies work. Training data can be defined as the initial set of data used to help AI & ML models understand how to apply technologies such as neural networks to learn and produce accurate results.
Training sets are materials through which an AI or ML models learn how to process information and produce the desired output. Machine learning uses neural network algorithms that mimic the abilities of the human brain to take in diverse inputs and weigh them, to produce neural activations, in individual neurons. These provide a highly detailed model of how human thought process works.
Given the diverse types of systems available, training datasets are structured in a different way for different models. For conversational bots, the training set contains the raw text that gets classified and manipulated.
On the other hand, for convolution models using image processing and computer vision, the training set consists of a large volume of images. Given the complexity and sophistication of these models, it uses iterative training on each image to eventually understand the patterns, shapes, and subjects in a given image.
In a nutshell, training sets are labeled and organized data needed to train AI and ML models.
Why Are Training Datasets Important
When building training sets for AI & ML models, one needs huge amounts of relevant data to help these models make the most optimal decision. Machine learning allows computer systems to tackle very complex problems and deal with inherent variations of hundreds and thousands or millions of variables.
The success of such models is highly reliant on the quality of the training set used. A training set that accounts for all variations of the variables in the real world would result in developing more accurate models. Just like in the case of a company collecting survey data to know about their consumer, larger the sample size for the survey is, more accurate the conclusion will be.
If the training set isn't large enough, the resultant system won't be able to capture all variations of the input variables resulting in inaccurate conclusions.
While AI & ML models need huge amounts of data, they also need the right kind of data, as the system learns from this set of data. Having a sophisticated algorithm for AI & ML models isn't enough when the data used to train these systems are bad or faulty. Training a system on a poor dataset or a dataset that contains wrong data, the system will end up learning wrong lessons, and generate wrong results. And eventually, not work the way it is expected to. On the contrary, a basic algorithm using a high-quality dataset will be able to produce accurate results and function as expected.
For example, in the case of a speech recognition system. The system can be made on a mathematical model to train the system on textbook English. However, this system is bound to show inaccurate results.
When we talk about language, there is a massive difference between textbook English and how people actually speak. To this add the factors – such as voice, dialects, age, gender – varying among speakers. This system would struggle to handle any cases or conversations that stray from the textbook English used to train it. For inputs having loose English or a different accent or use of slang, the system would fail to function for the purpose it was created.
Also, in a case, such a system is used to comprehend a text chat or email it would throw unexpected results. As a system trained in textbook English would fail to account for abbreviations and emojis used, which are commonly used among people in everyday conversations.
So, to build an accurate AI or ML model, it's essential to build a comprehensive and high-quality training dataset. To help these systems learn the right lessons and formulate the right responses. While it's a substantial task to generate such a high volume of data, it is necessary to do so.
How To Build A Training Dataset
Now, that we have understood why training data are integral to the success of an AI or ML model, it's necessary to know how to build a training dataset.
The process of building a training dataset can be classified into 3 simple steps: data collection, data preprocessing, and data conversion. Let's take a look at each of these steps and how it helps in building a high-quality training set.
The first step in making a training set is choosing the right number of features for a particular dataset. The data should be consistent and have the least amount of missing values. In case a feature has 25% to 30% of missing values, then this feature should not be considered to be part of the training set.
However, there might be instances when such features might be closely related to another feature. In such a case, it's advisable to impute and handle the missing values correctly to achieve desired results. At the end of the data collection step, you should clearly know how to handle preprocessing data.
Once the data has been collected, we enter the data preprocessing stage. In this step, we collect the right data from the complete data set and build a training set. The steps to be followed here are:
Organize and Format: If the data is scattered across multiple files or sheets, it's necessary to compile all this data to form a single dataset. This includes finding the relation between these datasets and preprocess to form a dataset of required dimensions.
Data Cleaning: Once all the scattered data is compiled to a single dataset, it's important to handle the missing values. And, remove any unwanted characters from the dataset.
Feature extraction: The final step in the data preprocessing step deals with finalizing the right number of features required for the training set. One has to analyze and find out features that are absolutely important for the model to function accurately and select them for faster computations and low memory consumption.
The data conversion stage consists of the following steps,
Scaling: Once the data is placed, it's necessary to scale the data as per a definite value. For example, a bank application containing transaction amount being important, then it's required to scale the data on transaction value to build a robust model.
Disintegration and composition: There might be certain features in the training data that can be better understood by the model when split. For example, time-series function, where days, month, year, hour, minutes, and seconds can be split for better processing.
Composition: While some features can be better utilized when disintegrated, other features can be better understood when combined with another.
This covers the necessary steps to be taken to build a high-quality training set for AI & ML models. While this might help you formulate a framework that helps you build training sets for your system, here's how you can put these frameworks into action.
Dedicated In-house Team
One of the easiest way for you could be to hire an intern to help you with the task of collecting and preprocessing data. You can also set up a dedicated ops team to help with your training set requirements. While this method provides you with greater control over the quality, it isn't scalable, and you'll be forced to look for more efficient methods eventually.
Outsource Training Set Creation
If having an in-house team doesn't cut it, it would be a smarter move to outsource it, right? Well, not entirely.
Outsourcing your training set creation has its own set of troubles. Right from training people to ensuring quality is maintained to making sure people aren't cutting slack.
Training Data Solutions Providers
With AI & ML technologies continuing to grow and more companies joining the bandwagon to roll out AI-enabled tools. There are a plethora of companies that can help you with your AI/ML training dataset requirement. We at Bridged.co have served prominent enterprises delivering over 50 million datasets.
And that is everything you need to know about training data, and how to go about creating one that helps you build powerful, robust, and accurate systems.
Understanding How Big Data Is Generated?
February 1, 2019 | All About Data, Artificial Intelligence, Machine Learning | No Comments
Why big data analytics is indispensable for today's businesses.
Ours is the age of information technology. Progress in IT has been exponential in the 21st century, and one direct consequence is the amount of data generated, consumed, and transferred. There's no denying that the next step in our technological advancement involves real-life implementations of artificial intelligence technology.
In fact, one could say we are already in the midst of it. And there's a definitive link between the large amounts of digital information being produced — called Big Data when it exceeds the processing capabilities of traditional database tools — and how new machine learning techniques use that data to assist the development of AI.
However, this isn't the only application of Big Data even if it has become the most promising. Big data analytics is now a heavily researched field which helps businesses uncover ground-breaking insights from the available data to make better and informed decisions. According to IDC, big data and analytics had market revenue of more than $150 billion worldwide in 2018.
What is the scale of data that we are dealing with today?
·It is estimated that there will be 10 billion mobile devices in use by 2020. This is more than the entire world population, and this is not including laptops and desktops.
We make over 1 billion Google searches every day.
Around 300 billion emails are sent every day.
More than 230 million tweets are written every day.
More than 30 petabytes (that's 1015 bytes) of user-generated data is stored, accessed and analyzed on Facebook.
On YouTube alone, 300 hours of video are uploaded every minute.
In just 5 years, the number of connected smart devices in the world will be more than 50 billion — all of which will collect, create, and share data.
Social media platforms have shot up human-generated data exponentially.
As an aside, in an attempt to impress the potential here, let me state that we analyze less than 1% of all available data. The numbers are staggering!
Before we get to classifying all this data, let us understand the three main characteristics of what makes big data big.
The 3 Vs of Big Data
Image Credit: workology
Volume refers to the amount of data generated through various sources. On social media sites, for example, we have 2 billion Facebook users, 1 billion on YouTube, and 1 billion together on Instagram and Twitter. The massive quantities of data contributed by all these users in terms of images, videos, messages, posts, tweets, etc. have pushed data analysis away from the now incapable excel sheets, databases, and other traditional tools toward big data analytics.
This is the speed at which data is being made available — the rate of transfer over servers and between users has increased to a point where it is impossible to control the information explosion. There is a need to address this with more equipped tools, and this comes under the realm of big data.
There are structured and unstructured data in all the content being generated. Pictures, videos, emails, tweets, posts, messages, etc. are unstructured. Sensor-collected data from the millions of connected devices is what you can call semi-structured while records maintained by businesses for transactions, storage, and analyzed unstructured information are part of structured data.
Classification of Big Data
With the amount of information that is available to us today, it is important to classify and understand the nature of different kinds of data and the requirements that go into the analysis for each.
Human Generated Data
Most human-generated data is unstructured. But this data has the potential to provide deep insights for heavy user-optimization. Product companies, customer service organizations, even political campaigns these days rely heavily on this type of random data to inform themselves of their audience and to target their marketing approach accordingly.
Image Credit: EMC
Machine Generated Data
Data created by various sensors, cameras, satellites, bio-informatic and health-care devices, audio and video analyzers, etc. combine to become the biggest source of data today. These can be extremely personalized in nature, or completely random. With the advent of internet-enabled smart devices, propagation of this data has become constant and omnipresent, providing user information with highly useful detail.
Data from Companies and Institutions
Records of finances, transactions, operations planning, demographic information, health-care records, etc. stored in relational databases are more structured and easily readable compared to disorganized online data. This data can be used to understand key performance indicators, estimate demands and shortage, prevalent factors, large-scale consumer mentality, and a lot more. This is the smallest portion of the data market but combined with consumer-centric analysis of unstructured data, can become a very powerful tool for businesses.
Whether one is seeking a profit advantage or a market edge, carving a niche product or capturing crowd sentiment, developing self-driving cars or facial recognition apps, building a futuristic robot or a military drone, big data is available for all sectors to take their technology to the next level. Bridged is a place where such fruitful experiments in data are being utilized and we are endeavoring to provide assistance to companies who are willing to take advantage of this untapped but currently mandatory investment in big data.
Drone Revolution
January 31, 2019 | Artificial Intelligence, Technology of Tomorrow | No Comments
It's a bird, it's a plane… Oh, wait it's a Drone!
Also known as Unmanned Aerial Vehicles (UAVs), drones have no human pilot onboard and are controlled by either a person with a remote control/smartphone on the ground or autonomously via a computer program.
These devices are already popular in various industries like Defense, Film making and Photography and are gaining popularity in fields like Farming, Atmospheric research, and Disaster relief. But even after so much innovation and experimentation, we have not explored the full capacity of data gained from drones.
We at Bridged AI are aware of this fact and are contributing to this revolution by helping the drone companies in perfecting their models by providing them with curated training data.
Impact of Drones
Drones inspecting power lines
Drones are being used by companies like GE to inspect their infrastructure, including power lines and pipelines. They can be used by companies and service organizations to provide instant surveillance in multiple locations instantly.
Surveillance by drones
They can be used for tasks like patrolling borders, tracking storms, and monitoring security. Drones are already being used by some defense services.
Border patrolling
Drones surveying farms
In agriculture, drones are used by farmers to analyze their farms for keeping a check on yield, unwanted plants or any other significant changes the crops go through.
Drones at their best
Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:
Drones use sensors such as electro-optical, stereo-optical, and LiDAR to perceive and absorb the environment or objects in some way.
Computer Vision is concerned with the automatic extraction, analysis, and understanding of useful information from one or more drone images.
Deep learning is a specialized method of information processing and a subset of machine learning that uses neural networks and huge amounts of data for decision-making.
DJI's Drone
Drones with Artificial Intelligence
The term Artificial intelligence is now routinely used in the Drone industry.
The goal of drones and artificial intelligence is to make efficient use of large data sets as automated and seamless as possible.
A large amount of data nowadays is collected by drones in different forms.
This amount of data is very difficult to handle, and proper tools and techniques are required to turn the data to a usable form.
Combination of drones with AI has turned out to be very astounding and indispensable.
AI describes the capability of machines that can perform sophisticated tasks which have characteristics of human intelligence and includes things like reasoning, problem-solving, planning and learning.
Future with Drones and AI
In just a few years, drones have influenced and redefined a variety of industries.
When on the one hand the business tycoons believe that automated drones are the future, on the other hand, many people are threatened by the possibility of this technology becoming wayward. This belief is inspired by many sci-fi movies like The Terminator, Blade Runner and recently Avengers: The Age of Ultron.
What happens when a robot develops a brain of its own? What happens if they realize their ascendancy? What happens if they start thinking of humans as an inferior race? What if they take up arms?!
"We do not have long to act," Elon Musk, Stephen Hawking, and 114 other specialists wrote. "Once this Pandora's box is opened, it will be hard to close."
Having said that, it is the inherent nature of humans to explore and invent. The possibilities that AI-powered drones bring along are too charming and exciting to let go.
At Bridged AI we are not only working on the goal of utilising AI-powered drone data but also helping other AI companies by creating curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.
Development of Artificial Intelligence — A Brief History
January 29, 2019 | Artificial Intelligence, Machine Learning, Technology of Tomorrow | No Comments
The Three Laws of Robotics — Handbook of Robotics, 56th Edition, 2058 A.D.
1. First Law — A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. Second Law — A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. Third Law — A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
Ever since Isaac Asimov penned down these fictional rules governing the behavior of intelligent robots — in 1942 — humanity has become fixated with the idea of making intelligent machines. After British mathematician Alan Turing devised the Turing Test as a benchmark for machines to be considered sufficiently smart, the term artificial intelligence was coined in 1956 at a summer conference in Dartmouth University, USA for the first time. Prominent scientists and researchers debated the best approaches to creating AI, favoring one that begins by teaching a computer the rules governing human behavior — using reason and logic to process available information.
There was plenty of hype and excitement about AI and several countries started funding research as well. Two decades in, the progress made did not deliver on the initial enthusiasm or have a major real-world implementation. Millions had been spent with nothing to show for it, and the promise of AI failed to become anything more substantial than programs learning to play chess and checkers. Funding for AI research was cut down heavily, and we had what was called an AI Winter which stalled further breakthroughs for several years.
Programmers then focused on smaller specialized tasks for AI to learn to solve. The reduced scale of ambition brought success back to the field. Researchers stopped trying to build artificial general intelligence that would implement human learning techniques and focused on solving particular problems. In 1997, for example, IBM supercomputer Deep Blue played and won against the then world chess champion Gary Kasparov. The achievement was still met with caution, as it showcased success only in a highly specialized problem with clear rules using more or less just a smart search algorithm.
The turn of the century changed the AI status quo for the better. A fundamental shift in approach was brought in that moved away from pre-programming a computer with rules of intelligent behavior, to training a computer to recognize patterns and relationships in data — machine learning. Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several 'nodes' that process information similar to how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning.
Different types of machine learning approaches were developed at this time:
Along with better-written algorithms, several other factors helped accelerate progress:
Exponential improvements in computing capability with the development of Graphical Processing Units (GPUs) and Tensor Processing Units have reduced training times and enabled implementation of more complex algorithms.
The availability of massive amounts of data today has also contributed to sharpening machine learning algorithms. The first significant phase of data creation happened with the spread of the internet, with large scale creation of documents and transactions. The next big leap was with the universal adoption of smartphones generating tons of disorganized data — images, music, videos, and docs. We have another phase of data explosion today with cloud networks and smart devices constantly collecting and storing digital information. With so much data available to train neural networks on potential scores of use-cases, significant milestones can be surpassed, and we are now witnessing the result of decades of optimistic strides.
Google has built autonomous cars.
Microsoft used machine learning to capture human movement in the development of Kinect for Xbox 360.
IBM's Watson defeated previous winners on the television show Jeopardy! where contestants need to come up with general knowledge questions based on given clues.
Apple's Siri, Amazon's Alexa, Google Voice Assistant, Microsoft's Cortana, etc. are well-equipped conversational AI assistants that process language and perform tasks based on voice commands.
AI is becoming capable of learning from scratch the best strategies and gameplay to defeat human players in multiple games — Chinese board game Go by Google DeepMind's AlphaGo, computer game DotA 2 by OpenAI are two prolific instances.
Alibaba language processing AI outscored top contestants in a reading and comprehension test conducted by Stanford University.
And most recently, Google Duplex has learned to use human-sounding speech almost flawlessly to make appointments over the phone for the user.
We have even created a Chatbot (called Eugene Goostman) that passed the Turing Test, 64 years after it was first proposed.
All the above examples are path-breaking in each field, but they also show the kind of specialized results that we have managed to attain. In addition, such achievements were realized only by organizations which have access to the best resources — finance, talent, hardware, and data. Building a humanoid bot which can be taught any task using a general artificial intelligence algorithm is still some distance away, but we are taking the right steps in that direction.
Bridged is helping companies realize their dream of developing AI bots and apps by taking care of their training data requirements. We create curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.
NLP in AI and the Realization of Futuristic Robots
January 28, 2019 | Artificial Intelligence, Natural Language Processing, Technology of Tomorrow | No Comments
How a well-trained conversational AI can empower your business
When the most valuable asset in the world is data, the most powerful tool you can have is the ability to process exabytes of information that data has to offer, and productively so. As we begin to produce gigabytes of digital data every day, De Toekomst — The Future — is with those that can effectively utilize this space, or more appropriately, the cloud. And it is precisely here that Artificial Intelligence is making its mark.
While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. And the sphere where AI is making its presence felt like a real and tangible entity is when it has a voice of its own. Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.
AI has grown to become our personal assistant helping us with tasks at our behest, literally. Apple's Siri, Amazon's Alexa, Microsoft's Cortana and Google Voice Assistant are only a few examples of AI systems integrating themselves seamlessly into our daily lives and routine. They help us plan our schedules, carry out functions without us having to push a single button, inform us of the latest developments, all the while learning more about our preferences and customizing themselves for us just by listening. With our permission, AI can become our best help.
How businesses are leveraging the AI assistant
Equipped with the knowledge of human communication, AI bots can potentially be used in any field that involves language to derive fast, intelligent, and useful insights which can then be transformed into follow-up actions tailored for each customer. Companies have realized the benefits of this incredibly powerful service and have begun utilizing them to gain significant market advantages. We will now talk about a few major applications of the conversational AI, and how we at Bridged are helping companies realize their ambitions for the AI-driven future.
Voice Control and Assistance
Performing basic tasks — reading messages, checking notifications, news updates, changing settings, operating connected devices, speech-to-text services.
Planning and Scheduling — setting up meetings, calendar events, automated replies, navigation, online assistance, payments.
Personalization and Security — compiling playlists, product suggestions, mood-based ambiance control, surveillance, and security.
Bridged.co Services: Voice Recognition, Speech Synthesis, Search Relevance.
Chat-bots
An ever-present agent ready to listen to your needs complaints and thoughts, and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
Bridged.co Services: Chat-bot Training, Virtual Assistant Training, NLP.
The ability to monitor end-user opinions of a brand or product and gain an understanding of the same on a large scale is clutch in any competitive scenario. Customer retention has become a zero-sum game and sentiment analysis stands at the center of this marketing field. Armed with NLP and machine learning, AI can listen to the scores of available user opinions across multiple platforms be it social media or community forums or even personal blogs. Accurate analyses of brand value at scale provided by accurate AI are invaluable to businesses.
Bridged.co Services: Brand Sentiment Analysis, E-commerce Recommendations, User Content Support.
AI is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. AI can be put up to a) responding to common queries, b) as a first layer of gathering service request info and routine troubleshooting, c) integrating with the resolution system, learning from successful cases, and suggesting or implementing final calls. AI makes the whole system faster and more efficient.
Bridged.co Services: Chat-bot Training, Sentiment Analysis, User Content Support.
Translate languages as you speak
The need for a multi-language translation book or for a local guide to communicating your need in a tongue you don't speak is reduced with the advent of live translation by conversation bots that speak your message out loud, as and when you call on them right from your phones and smart devices.
Bridged.co Services: NLP, Voice Recognition, Speech Analysis.
Real-time Transcription
You can count on AI to take down notes for when you are in meetings or need to parse audio or video clips, or just want to pen down your thoughts. Transcription of speech to text is a very common application and finds use in several business tasks.
Bridged.co Services: Audio/Video Transcription, NLP, Voice Recognition.
We are at a very exciting juncture in the development of AI technology. New machine learning techniques including deep learning applied to NLP processes have made it possible to stretch the boundaries of what can be built using AI bots.


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struct csn_api_config {
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} conf;

struct csn_api_config readconfig(const char* filename);
void writeconfig(const char* filename, struct csn_api_config myconfig);

#endif // CONF_H_INCLUDED

redpajama_set_name: RedPajamaGithub


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The Challenge's vision is a more integrated society where there is understanding and appreciation of each other's differences. We design and deliver programmes that bring different people together to develop their confidence and skills in understanding and connecting with others. We also develop ideas to forge a more socially integrated Britain. During 2014 and 2015, The Challenge convened the Social Integration Commission. Following the Commission's conclusion, we set up the All Party Parliamentary Group (APPG) on Social Integration, which is chaired by Chuka Umunna MP.
We believe everyone benefits when people from all walks of life come together to better know, understand and trust each other. This also applies to the people we employ to work with us. As an equal opportunities employer, our aim is to have a diverse workforce to reflect the communities we work in.
This is a unique opportunity to join a dynamic organisation, which works with young people and local communities across the country. For more information about what The Challenge does and what its values are, please visit our website.
The Research Analyst will support the production of original research and policy analysis on topics and themes related to social integration, mixing and connectivity, including supporting the impact evaluation of The Challenge's key programmes. The successful candidate will work to the Senior Policy, Research & Evaluation Manager, and will deliver rigorous and impactful research content, which provides evidence for The Challenge's mission to promote a more socially integrated society, and which supports the Public Affairs and Policy team to position The Challenge as a thought leader.
Undertaking research and analysis, using a broad range of data sources, towards the production of authoritative, impactful and rigorous policy and research reports on topics and themes related to social mixing, connection and integration.
Drafting articles, blogs and essays on research insights and policy recommendations resulting from this work, and representing the organisation at events and conferences.
Support the Senior Policy, Research & Evaluation Manager in the primary analysis of impact data of The Challenge's programmes towards the production of impact reports.
Support the work of the wider Public Affairs and Policy team in its role as the Secretariat for the APPG on Social Integration – such as highlighting research relevant to the APPG's current interests, and proofing draft reports.
Proactively monitor for research output relevant to The Challenge's mission and programmes.
Understanding of qualitative and quantitative research methods.
The ability to analyse and interpret complex quantitative data.
Experience with statistical programming tools (e.g. Excel, R).
A good understanding of UK demographic data sources, including their advantages and disadvantages.
Very strong written and verbal communications skills, including clear and concise presentation and reporting of quantitative research data.
An ability to juggle numerous tasks and prioritise effectively.
A good understanding of UK politics, policy debates and government structures.
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The IWEA offers an associate category of membership, which includes individuals, firms, partnerships, or corporations that have a direct relationship with our membership by supplying products or services used at various construction sites throughout the region.
Below you will find a listing of all associate members as well as links to their backgrounds and contact information.
ALL Crane Rental of Pennsylvania, LLC.
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Aetion
Engineering Prospects
3+ months ago•Boston, MA
(+2 more)
New York, NYEl Segundo, CA
Aetion is advancing the global adoption of real-world evidence for critical health care decisions.
Size: 51 - 200 employees
While we may not have a specific role open today, we are still interested in building strong relationships with software engineering candidates for future opportunities. If you are interested in working with our technology team, please fill out an Engineer prospect application and we are excited to keep in touch.
Welcome to Aetion! Since our debut in 2013, we have grown into one of the country's leading science-driven technology companies using real-world evidence to provide innovative healthcare solutions.
We achieve this with our Aetion Evidence Platform, a software platform used to evaluate the safety, effectiveness and value of medications, delivering better outcomes to patients, medical professionals and clients. We've partnered with top biopharma companies and are backed by leading venture capital firms to help increase our medical research and expand our product line.
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If that's you, we'd love to hear from you.
About the Engineering Team
Our team is working on a full stack rapid-cycle analytic application handling large amounts of data (10+ million patient records). The team works on a technical stack which includes both cloud and on-premise deployments, big-data ingestion and analytics, distributed systems and algorithmic complexity. We collaborate cross-functionally to develop transformation logic against disparate datasets into Aetion's proprietary format, which is in SQL and Scala UDF and executed over a Spark cluster. We also utilize Spark/Databricks and a combination of Java/Scala to develop our data ingestion infrastructure. Check out our full tech stack below!
Version Control: Github
Language: Java (JDK 8), Python, Scala, SQL
Big Data Processing: Apache Spark, Databricks, Airflow
Framework: Dropwizard, REACT, Spring, Hibernate
Database: MySQL, RedShift,
Build Tools: Maven, Sbt, Jenkins, AWS CodeBuild
Infrastructure: AWS
Container: Docker
IDE: IntelliJ
Configuration Tools: Chef, Terraform
Kindly Please Note! If you submit your application today, we will review your candidacy and reach out when a role becomes available!
About Aetion
Lead at all levels – Aetion is a diverse workforce of scientific thought leaders and technological innovators coming together with a vision to dramatically improve the Healthcare industry. Aetion supports and maintains a presence in organizations such as ISPOR, ISPE, ASHP, and HIMSS.
Located in three cities — We have offices located in Midtown Manhattan near Penn Station, Boston's financial district, and Los Angeles. All locations are accessible by various forms of public transportation.
Social and energetic offices – with a modern layout, and a giant kitchen and eating/social area. We have an open floor plan with an abundance of conference rooms, designed for impromptu collaboration, company gatherings, and industry meetups and events.
Great perks – We offer a sabbatical after five years, competitive benefits such as 16 weeks parental leave from day one, stock options, and unlimited PTO.
Company Accolades – Aetion is the recipient of numerous prestigious awards:
Aetion was listed among Top 10 employers on Fast Company's 100 Best Workplaces for Innovators list
CB Insights Digital Health 150 showcases Aetion's work advancing global RWE among some of the most promising private digital health companies in the world
Databricks Data Team Award recognizes Aetion's platform analyzing COVID-19 data.
Parity.org's 2020 Best Companies for Women to Advance highlights Aetion's efforts and commitment to close the gender gap in leadership!
Aetion is an Equal Opportunity Employer. Aetion is committed to being an employer of choice, not just a good place to work, but a great and inclusive place to work. To that end, we strive to recruit and maintain a workforce that meaningfully represents the diverse and culturally rich communities that we serve. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, sexual orientation, gender identity, protected veteran status or disabled status or, genetic information.
Job ID: 2184000

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