Pharmaceutical professionals believe artificial intelligence (AI)will be the most disruptive technology in the industry in 2021. As AI and machine learning (ML) become crucial tools for keeping pace in the industry, clinical development is an area that can substantially benefit, delivering significant time and cost efficiencies while providing better, faster insights to inform decision making. However, for patients, these tools provide improved safety practices that lead to better, safer, drugs. Here is how AI/ML can be used to support pharma companies in delivering safer drugs to market.

Overcoming Barriers to Using AI in Clinical Research

Today, AI and ML can be used to support clinical research in numerous ways; including the identification of molecules that hold potential for clinical treatments, finding patient populations that meet specific criteria for inclusion or exclusion, as well as analyzing scans, claims reports, and other healthcare data to identify trends in clinical research and treatments that lead to safer and faster decisions.

However, to take full advantage of the benefits of AI/ML technology, organizations performing clinical trials must first gain access to the tools, expertise, and industry-specific datasets enabling them to build algorithms to fit their specific needs. Healthcare data, unlike purely numerical data pulled from monitoring systems and tools such as IoT or SaaS platforms, is typically unstructured due to the way the data is collected (through doctor visits, and unstructured web sources) and must meet strict security protocols to ensure patient privacy.

To truly leverage AI and ML for clinical research, data must be collected, studied, combined, and protected to make effective healthcare decisions. When clinical researchers collaborate with partners that have both technical and pharmaceutical expertise, they ensure that data is being structured and analyzed in a way that simultaneously reduces risks and improves the quality of clinical research.

The Benefits of AI for Clinical Research

When it comes to research study design, site identification and patient recruitment, and clinical monitoring, AI and ML hold great potential to make clinical trials faster, more efficient, and most importantly: safer.

Study design sets the stage for a clinical research initiative. The cost, efficiency, and potential success of clinical trials rest squarely on the shoulders of the study’s design and plans. AI and ML tools, along with natural language processing (NLP), can analyze large sets of healthcare data to assess and identify primary and secondary endpoints in clinical research design. This ensures that protocols for regulators, payers, and patients are well defined before clinical trials commence. Defining parameters such as these optimize study design by helping to identify ideal research sites and enrollment models. Ultimately, better study design leads to more predictable results, reduced cycle time for protocol development, and a generally more efficient study.

Identifying trials sites and recruiting patients for clinical research is a tougher task than it seems to be at face value. Clinical researchers must identify the area that will provide enough access to patients who meet inclusion and exclusion criteria. As studies become more focused on rarer conditions or specific populations, recruiting participants for clinical trials becomes more difficult, which increases the cost, timeline, and risk of failure for the clinical study if enough patients cannot be recruited for the research. AI and ML tools can support site identification for clinical research by mapping patient populations and proactively targeting sites with the most potential patients that meet inclusion criteria. This enables fewer research sites to meet recruitment requirements and reduce the overall cost of patient recruitment.

Clinical monitoring is a tedious manual process of analyzing site risks of clinical research and determining specific actions to take towards mitigating those risks. Risks in clinical research include recruitment or performance issues, as well as risks to patient safety. AI and ML automate the assessment of risks in the clinical research environment, and provide suggestions based on predictive analytics to better monitor for and prevent risks. Automating this assessment removes the risk of manual error, and decreases the time spent on analyzing clinical research data.

Strategies for Using AI for Clinical Research

During clinical trials, there’s a limited patient population to pull from, as research subjects must meet pre-set parameters for inclusion in the study. On the other hand, as opposed to post-market research, clinical researchers are blessed with vast amounts of information surrounding their patients including what drugs they are taking, their health history, and their current environment.

In addition, because the clinical researcher is working closely with the patient and is well-educated on the drug or product being researched, the researcher is very familiar with all potential variables involved in the clinical trial. To put it simply, clinical trials have a lot of information to analyze, but few patients with whom to conduct the research. Because of this disproportionate ratio of information over patients, every case in a clinical research setting is extremely important to the future of the drug being researched.

The massive amount of patient and drug information available to clinical researchers necessitates the use of NLP tools to analyze and process documents and patient records.NLP can search documents and records for specific terms, phrases, and words that might indicate a problem or risk in the clinical trial. This eliminates the need for manual analysis of clinical trial data – reducing, and in some cases eliminating, the risk of human error while also increasing patient safety. This is especially useful in lengthy clinical trials, for which researchers will need to analyze patient histories and drug results over an extended period of time. Many clinical trials have long document trails and questionnaires that can add up to hundreds of pages of patient data that researchers must analyze.

In a clinical trial, researchers are ultimately trying to determine whether the benefits of a specific treatment outweigh the risks. AI can be especially helpful in clinical trials of high-risk drugs. If a researcher knows that a drug cures or alleviates an illness or condition, but also know that the potential side effects of that drug can have a significant negative impact on the patient, they’ll want to know how to determine if a patient is likely to present those negative side effects. NLP can be used to produce word clouds of potential signals of the negative side effects of a drug that patients would experience.

The only way to do this type of analysis manually is to identify those words using human researchers, then analyze the patient reports to find those words, and group those reports into risk profiles. NLP can automate that entire process and provide insights on risk indicators in patients much more efficiently and safely than human researchers ever could.

Integrating AI & ML with Clinical Research Creates Competitive Results

AI and ML technologies, especially NLP, hold huge promise to support and optimize clinical research. However, that assurance can only be achieved by organizations that have the necessary tools, expertise, and partners to leverage the full benefits of AI and ML. AI and ML solutions support the optimization of clinical research by more efficiently analyzing research data for risks and allowing faster trial planning and research. Those who fail to engage AI and ML for clinical research may find that their competitors are doing so, and as a result, are going to market with new drugs and products faster with higher profits due to decreased research time and safer practices.

Author

Updesh Dosanjh, Practice Leader, Pharmacovigilance Technology Solutions, IQVIA

As Practice Leader for the Technology Solutions business unit of IQVIA, Updesh Dosanjh is responsible for developing the overarching strategy regarding Artificial Intelligence and Machine Learning as it relates to safety and pharmacovigilance. He is focused on the adoption of these innovative technologies and processes that will help optimize pharmacovigilance activities for better, faster results.  Dosanjh has over 25 years of knowledge and experience in the management, development, implementation, and operation of processes and systems within the life sciences and other industries.  Most recently, Dosanjh was with Foresight and joined IQVIA as a result of an acquisition. Over the course of his career, Dosanjh also worked with WCI, Logistics Consulting Partners, Amersys Systems Limited, and FJ Systems. Dosanjh holds a Bachelor’s degree in Materials Science from Manchester University and a Master’s degree in Advanced Manufacturing Systems and Technology from Liverpool University.

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Artificial life may be labeled software, hardware, or wetware, depending on the type of media researchers work with

Artificial life is devoted to the study and creation of life like structures in various media (computational, biochemical, mechanical, or combinations of these). A central aim is to model and even realize emergent properties of life, such as self-reproduction, growth, development, evolution, learning, and adaptive behavior. Researchers of artificial life also hope to gain general insights about self-organizing systems, and to use the approaches and principles in technology development.  Evolution of research The historical and theoretical roots of the field are manifold. These roots include: (1) Early attempts to imitate the behavior of humans and animals by the invention of mechanical automata in the sixteenth century.(2) Cybernetics as the study of general principles of informational control in machines and animals. (3) Computer science as theory and the idea of abstract equivalence between various ways to express the notion of computation, including physical instantiations of systems performing computations. (4) Computer science as a set of technical practices and computational architectures.(5) Artificial intelligence (AI) and robotics. Despite the field’s long history, the first international conference for artificial life was not held until 1987. Computer scientist C. G. Langton, who sketched a future synthesis of the field’s various roots and formulated important elements of a research program, organized the conference. As in artificial intelligence research, some areas of artificial life research are mainly motivated by the attempt to develop more efficient technological applications by using biologic inspired principles. Examples of such applications include modeling architectures to simulate complex adaptive systems, as in traffic planning, and biologically inspired immune systems for computers. Other areas of research are driven by theoretical questions about the nature of emergence, the origin of life, and forms of self-organization, growth, and complexity.  The media of artificial life Artificial life may be labeled software, hardware, or wetware, depending on the type of media researchers work with. Software artificial life is rooted in computer science and represents the idea that form, or forms of organization, rather than characterize life by its constituent material. Thus, “life” may be realized in some form (or media) other than carbon chemistry, such as in a computer’s central processing unit, or in a network of computers, or as computer viruses spreading through the Internet. One can build a virtual ecosystem and let small component programs represent species of prey and predator organisms competing or co- operating for resources like food.  The difference between this type of artificial life and ordinary scientific use of computer simulations is that, with the latter, the researcher attempts to create a model of a real biological system (e.g., fish populations of the Atlantic Ocean) and to base the description upon real data and established biologic principles. The researcher tries to validate the model to make sure that it represents aspects of the real world. Conversely, an artificial life model represents biology in a more abstract sense; it is not a real system, but a virtual one, constructed for a specific purpose, such as investigating the efficiency of an evolutionary process of a Lamarckian type (based upon the inheritance of acquired characters) as opposed to Darwinian evolution (based upon natural selection among randomly produced variants). Such a biologic system may not exist anywhere in the real universe. Artificial life investigates “the biology of the possible” to remedy one of the in- adequacies of traditional biology, which is bound to investigate how life actually evolved on Earth, but cannot describe the borders between possible and impossible forms of biologic processes. For example, an artificial life system might be used to determine whether it is only by historical accident that organisms on Earth have the universal genetic code that they have, or whether the code could have been different.  It has been much debated whether virtual life in computers is nothing but a model on a higher level of abstraction, or whether it is a form of genuine life, as some artificial life researchers maintain. In its computational version, this claim implies a form of Platonism whereby life is regarded as a radically medium-independent form of existence similar to futuristic scenarios of disembodied forms of cognition and AI that may be downloaded to robots. In this debate, classical philosophical issues about dualism, monism, materialism, and the nature of information are at stake, and there is no clear-cut demarcation between science, metaphysics, and issues of religion and ethics. If it really is possible to create genuine life “from scratch” in other media, the ethical concerns related to this research are intensified: In what sense can the human community be said to be in charge of creating life de novo by non-natural means?  Hardware artificial life refers to small animal-like robots, usually called animats, that researchers build and use to study the design principles of autonomous systems or agents. The functionality of an agent (a collection of modules, each with its own domain of interaction or competence) is an emergent property of the intensive interaction of the system with its dynamic environment. The modules operate quasi-autonomously and are solely responsible for the sensing, modeling, computing or reasoning, and motor control that is necessary to achieve their specific competence. Direct coupling of perception to action is facilitated by the use of reasoning methods, which operate on representations that are close to the information of the sensors.  This approach states that to build a system that is intelligent, it is necessary to have its representations grounded in the physical world. Representations do not need to be explicit and stable, but must be situated and “embodied.” The robots are thus situated in a world; they do not deal with abstract descriptions, but with the environment that directly influences the behavior of the system. In addition, the robots have “bodies” and experience the world directly, so that their actions have an immediate feedback upon the robot’s own sensations. Computer-simulated robots, on the other hand, may be “situated” in a virtual environment, but they are not embodied. Hardware artificial life has many industrial and military technological applications.  Wetware artificial life comes closest to real biology. The scientific approach involves conducting experiments with populations of real organic macromolecules (combined in a liquid medium) in order to study their emergent self- organizing properties. An example is the artificial evolution of ribonucleic acid molecules (RNA) with specific catalytic properties. (This research may be useful in a medical context or may help shed light on the origin of life on Earth.) Research into RNA and similar scientific programs, however, often take place in the areas of molecular biology, biochemistry and combinatorial chemistry, and other carbon-based chemistries. Such wetware research does not necessarily have a commitment to the idea, often assumed by researchers in software artificial life, that life is a composed of medium-in- dependent forms of existence.  Thus wetware artificial life is concerned with the study of self-organizing principles in “real chemistries.” In theoretical biology, ‘autopoiesis’is a term for the specific kind of self-maintenance produced by networks of components producing their own components and the boundaries of the network in processes that resemble organizationally closed loops. Such systems have been created artificially by chemical components not known in living organisms.  Conclusion Questions of theology are rarely discussed in artificial life research, but the very idea of a human researcher “playing God” by creating a virtual universe for doing experiments (in the computer or the test tube) with the laws of growth, development, and evolution shows that some motivation for scientific research may still be implicitly connected to religious metaphors and modes of thought.

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Learn how to use machine learning for more precise, statistically relevant, and scalable SEO competitor research (with tools, code & more).

With the ever-increasing appetite of SEO professionals to learn Python, there’s never been a better or more exciting time to take advantage of machine learning’s (ML) capabilities and apply these to SEO.

This is especially true in your competitor research.

In this column, you’ll learn how machine learning helps address common challenges in SEO competitor research, how to set up and train your ML model, how to automate your analysis, and more.

Let’s do this!

Why We Need Machine Learning in SEO Competitor Research

Most if not all SEO pros working in competitive markets will analyze the SERPs and their business competitors to find out what it is their site is doing to achieve a higher rank.

Back in 2003, we used spreadsheets to collect data from SERPs, with columns representing different aspects of the competition such as the number of links to the home page, number of pages, etc.

In hindsight, the idea was right but the execution was hopeless due to the limitations of Excel in performing a statistically robust analysis in the short time required.

And if the limits of spreadsheets weren’t enough, the landscape has moved on quite a bit since then as we now have:

• Mobile SERPs.
• Social media.
• A much more sophisticated Google Search experience.
• Page Speed.
• Personalized search.
• Schema.
• Javascript frameworks and other new web technologies.

The above is by no means an exhaustive list of trends but serves to illustrate the ever-increasing range of factors that can explain the advantage of your higher-ranked competitors in Google.

Machine Learning in the SEO Context

Thankfully, with tools like Python/R, we’re no longer subject to the limits of spreadsheets. Python/R can handle millions to billions of rows of data.

If anything, the limit is the quality of data you can feed into your ML model and the intelligent questions you ask of your data.

As an SEO professional, you can make the decisive difference to your SEO campaign by cutting through the noise and using machine learning on competitor data to discover:

• Which ranking factors can best explain the differences in rankings between sites.
• What the winning benchmark is.
• How much a unit change in the factor is worth in terms of rank.

Like any (data) science endeavor, there are a number of questions to be answered before we can start coding.

What Type of ML Problem is Competitor Analysis?

ML solves a number of problems whether it’s categorizing things (classification) or predicting a continuous number (regression).

In our particular case, since the quality of a competitor’s SEO is denoted by its rank in Google, and that rank is a continuous number, then the ML problem is one of regression.

Outcome Metric

Given that we know the ML problem is one of regression, the outcome metric is rank. This makes sense for a number of reasons:

• Rank won’t suffer from seasonality; an ice cream brand’s rankings for searches on [ice cream] won’t depreciate because it’s winter, unlike the “users” metric.
• Competitor rank is third-party data and is available using commercial SEO tools, unlike their user traffic and conversions.

What Are the Features?

Knowing the outcome metric, we must now determine the independent variables or model inputs also known as features. The data types for the feature will vary, for example:

• First paint measured in seconds would be a numeric.
• Sentiment with the categories positive, neutral, and negative would be a factor.

Naturally, you want to cover as many meaningful features as possible including technical, content/UX, and offsite for the most comprehensive competitor research.

What Is the Math?

Given that rankings are numeric, and that we want to explain the difference in rank, then in mathematical terms:

rank ~ w_1*feature_1 + w_2*feature_2 + … + w_n*feature_n

~ (known as the “tilde”) means “explained by”

n being the nth feature

w is the weighting of the feature

Using Machine Learning to Uncover Competitor Secrets

With the answers to these questions in hand, we’re ready to see what secrets machine learning can reveal about your competition.

At this point, we will assume that your data (known in this example as “serps_data”) has been joined, transformed, cleaned, and is now ready for modeling.

As a minimum, this data will contain the Google rank and feature data you want to test.

For example, your columns could include:

• Google_rank.
• Page_speed.
• Sentiment.
• Flesch_kincaid_reading_ease.
• Amp_version_available.
• Site_depth.
• Internal_page_rank.
• Referring_comains count.
• avg_domain_authority_backlinks.
• title_keyword_string_distance.

Training Your ML Model

To train your model, we’re using XGBoost because it tends to deliver better results than other ML models.

Alternatives you may wish to trial in parallel are LightGBM (especially for much larger datasets), RandomForest, and Adaboost.

Try using the following Python code for XGBoost for your SERPs dataset:

# import the libraries

import xgboost as xgb

import pandas as pd

serps_data = pd.read_csv('serps_data.csv')

# set the model variables

# your SERPs data with everything but the google_rank column

serp_features = serps_data.drop(columns = ['Google_rank'])

# your SERPs data with just the google_rank column

rank_actual = serps_data.Google_rank

# Instantiate the model

serps_model = xgb.XGBRegressor(objective='reg:linear', random_state=1231)

# fit the model

serps_model.fit(serp_features, rank_actual)

# generate the model predictions

rank_pred = serps_model.predict(serp_features)

# evaluate the model accuracy

mse = mean_squared_error(rank_actual, rank_pred)

Note that the above is very basic. In a real client scenario, you’d want to trial a number of model algorithms on a training data sample (about 80% of the data), evaluate (using the remaining 20% data), and select the best model.

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Artificial intelligence can be used to improve the research method of Quantum Mechanics.

Searching for different uses of artificial intelligence has always been a successful journey and among its numerous uses, quantum mechanics stands in a vital position. Artificial Intelligence can be used to predict molecular wave functions and the electronic properties of molecules. The behavior of the electron in the molecule can be observed and the data can be fed to AI algorithm, which would further predict the future behaviors of the electrons in the molecules. The researchers of University of Warwick, the Technical University of Berlin and the University of Luxembourg have together come up with such innovative ways of using AI. Using quantum mechanics, the behavior of an electron in a molecule is still described by a wave function, analogous to the behavior in an atom. Just like electrons around isolated atoms, electrons around atoms in molecules are limited to discrete (quantized) energies. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Like an atomic orbital, a molecular orbital is full when it contains two electrons with opposite spin.

Making a difference

In general, artificial intelligence can be used in observing and predicting any consistent common behavior. For example, AI is used in predicting the shopping behavior of people and it is done by observing the way the person shops on a regular basis. In a similar way, AI can be used for predicting the quantum states of molecules, so-called wave functions, which determine all properties of molecules. AI is capable of doing this by learning to solve fundamental equations of quantum mechanics. Doing it in the conventional way requires massive high-performance computing resources, which is typically the bottleneck to the computational design of new purpose-built molecules for medical and industrial applications. However, this newly developed AI algorithm will be able to supply accurate predictions within seconds on a laptop or mobile phone.

Details of Research

Dr. Reinhard Maurer from the Department of Chemistry at the University of Warwick stated while talking about this research, “This has been a joint three year effort, which required computer science know-how to develop an artificial intelligence algorithm flexible enough to capture the shape and behavior of wave functions, but also chemistry and physics know-how to process and represent quantum chemical data in a form that is manageable for the algorithm.” The research shows that AI methods can efficiently perform the most difficult aspects of quantum molecular simulations. Within the next few years, AI methods will establish themselves as an essential part of the discovery process in computational chemistry and molecular physics. The team has been brought together during an interdisciplinary 3-month fellowship program at IPAM (UCLA) on the subject of machine learning in quantum physics.

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Vanderbilt’s Data Science Institute has kicked off the third year of its undergraduate summer research program. The program engages students who are interested in carrying out data science-related research with a Vanderbilt faculty member and integrates them into the institute’s community of data science scholars.

This year, the DSI awarded fellowships to 24 undergraduate students with diverse academic backgrounds and research interests. New this summer is the addition of four partner sponsors from across campus to support a number of fellows. These partnerships allow students to work in labs affiliated with their sponsor on projects using data science techniques while simultaneously taking advantage of workshops, demo sessions and curated feedback from the DSI team.

The partner sponsors are as follows:

• Vanderbilt Microbiome Initiative
• Evolutionary Studies Initiative at Vanderbilt
• Vanderbilt Brain Institute
• Frist Center for Autism and Innovation

The undergraduate fellows will engage in 10 weeks of research with their faculty mentor and enhance their data science skills through online workshops led by the data science team with a goal of becoming articulate leaders through weekly demonstrations.

The 2021 fellows come from a wide range of schools, departments and majors across campus, including anthropology, cognitive studies, physics, computer science, neuroscience, psychology, political science, economics, and medicine, health and society.

Learn more about the 2021 fellows.

2021 Fellows

• Katharine Cella
• Zoe Crawley
• Dillon Davey
• Andrew Gothard
• Jiaxin He
• Miya Hugaboom
• Chetan Immanneni
• Qiaochu Jiang
• Rohit Khurana
• Hangling Liu
• Suiyang Mai
• Jessica Mo
• Amy Rieth
• Joseph Sexton
• Elijah Sheridan
• Janet Stefanov
• Benjamin Van Sleen
• Chet Weissberg
• Aidan Wells
• Qinlian Yang
• Yuqin Yang
• Xinxin Zhang
• Qianhui Zheng
• Zheyu Zhu

Director of Undergraduate Research Thomas Palmeri, Chief Data Scientist Jesse Spencer-Smith and Assistant Director Amanda Harding lead the DSI-SRP.

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Half (49%) of young people fail to consider data science as a career option

Nearly ten years ago, Harvard Business Review famously labelled data science the sexiest job of the 21^st century. However new research from Exasol, the leading high performance analytics database company, suggests data science careers are rapidly falling off young people’s radars — despite data roles ticking a lot of boxes in terms of the skills and activities young people want from a future career, businesses and educators aren’t communicating the importance and application of data in easy to understand terms.  

Exasol’s research of more than 1000 16- to 21-year-olds in the UK (coined D/NATIVES by Exasol because of their everyday digital skills) found that half (49%) don’t consider data science as a career option. When asked about their future plans, 61% of D/NATIVES say they have a clear vision for their career. The most popular skills they want to feature prominently are communicating (39%); decision making (34%); problem solving (33%); finding information (32%); asking questions (30%); telling stories (23%); and maths (20%). All very relevant data science skills. In fact, they all align to five of the top words D/NATIVES used to describe the key characteristics of a data scientist; mathematical, problem solving, analytical, intelligence and confidence.  

Yet, despite clear awareness of the impact data and statistics have on their life, many young people are not familiar with jargon such as data literacy (51%) or big data (50%), demonstrating a clear disconnect between the language D/NATIVES use and the business words used by employers to advertise data careers, leading them to fail to consider the data science field as a career option. 

Commenting on the findings, Peter Jackson, Exasol CDO said,

“Ten years ago, data scientists were in demand thanks to their ability to plug a crucial skills gap and tackle new organisational challenges stemming from the growth of business data. Today, the demand for data scientists and data engineers has more than tripled since 2013.”

The good news is that the respondents to Exasol’s survey possess a lot of relevant soft skills that are crucial in helping organisations realise the full value of their data. Peter Jackson added, “In data teams there is room for all sorts of people, from the technical masterminds to the data storytellers that articulate the meaning of that data and turn them into actionable insights for the business.”

There is work for businesses and educators to do to make young people aware of the context, opportunities and risks surrounding data – demonstrating its power in our everyday lives. How we collect data, how and when we give it away, how it’s used and the potential it has never been more exciting and can no longer be shunned as a career option.

“D/NATIVES have untapped subconscious and habitual data literacy skills ideal for data analysis, storytelling and visualisation of key trends, patterns, and anomalies. Without these future data champions, businesses are in danger of missing out on new ways to solve data challenges today and pushing the boundaries of industry as we know it,” Peter concluded.  

More insights into the attitudes and understanding that young people currently in higher education or just entering the world of work have towards data can be found in Exasol’s report:

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The global Storage in Big Data Market was accounted for US$ 17,391.4 Mn in terms of value in 2019 and is expected to grow at CAGR of 20.4% for the period 2020-2027.

Big data storage refers to a compute and storage architecture that gathers and operates vast data sets and allows real-time data analytics. Many companies employ big data analytics to collect greater intelligence from metadata. Big data storage allows the storage and sorting of big data in such a way that it can be easily used, accessed, and processed by applications and services working on big data. Moreover, big data can be flexibly scaled as required. Many end-use industries employ big data storage including BFIS, media and entertainment, IT and telecommunications, healthcare and medical, transportation, logistics, retail, etc.

In a software-based storage solution, the storage controller software is disassociated from hardware and takes advantage of industry-standard hardware platforms, in order to deliver a complete range of storage services. This allows different solutions for data storage, data access interfaces, services, and can be delivered in various forms including on cloud. According to Intel Corporation’s study 2016, enterprises are shifting towards software-based storage as performance, capital expenses and scaling are the top three factors considered by data center managers. However, there are several approaches that can be used while deploying software-based storage such as Do-It-Yourself solutions, turnkey solutions and converged and hyper-converged solutions. Hence, these factors are expected to support growth of the global storage in the big data market in the near future.

Which are Compay Profile Plays Major Role in Storage in Big Data (AML) Market?
Key players operating in the global storage in big data market are MemSQL Inc., Google Inc., Hitachi Data Systems Corporation, Microsoft Corporation, Hewlett Packard Enterprise, Amazon Web Services, Inc., Teradata Corporation, VMware, Inc., SAP SE, IBM Corporation, Oracle Corporation, Dell EMC, and SAS Institute Inc.

What are the Key Segments in the Market By Types?
Global Storage in Big Data Market, By Segment:
• Hardware
◦ DAS – internal (OEM)
◦ DAS – external (OEM)
◦ DAS – other (ODM Direct)
◦ ESCON/FICON
◦ NAS
◦ SAN
◦ Tape Systems and Media
• Software
• Services

What are the Key Segments in the Market By End-use Sector ?
Global Storage in Big Data Market, By Industry:
• BFSI
• IT and Telecommunications
• Transportation, Logistics & Retail
• Healthcare and Medical
• Media and Entertainment
• Others

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Artificial intelligence research is increasingly influencing the use of technology

From our smartphones to cars and homes, artificial intelligence is increasingly touching our every walk of life. Applications of artificial intelligence have already proved disruptive across diverse industries, including manufacturing, healthcare, retail, etc. Considering these progresses, we can say artificial intelligence has evolved much impressively in recent years. Research around this technology has also surged and is impacting the way every individual and business interacts with AI technologies. Analytics Insight has listed 10 must look artificial intelligence research papers so far worth looking at now.

Adam: A Method for Stochastic Optimization

Author(s): Diederik P. Kingma, Jimmy Ba

Adam is an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, and it is computationally efficient, invariant to a diagonal rescaling of the gradients, and has little memory requirements. It is well suited for problems that are large in terms of data and parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. Adam has been adopted as a default method of optimization algorithm for all those millions of neural networks that people train nowadays.

Towards a Human-like Open-Domain Chatbot

Author(s): Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, RomalThoppilan, Zi Yang, ApoorvKulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le

This research paper presents Meena, a multi-turn open-domain chatbot that is trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize the perplexity of the next token. The researchers also propose a new human evaluation metric to capture key elements of a human-like multi-turn conversation, dubbed Sensibleness and Specificity Average (SSA).

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Author(s): Sergey Ioffe, Christian Szegedy

Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. The researchers refer to this phenomenon as “internal covariate shift”, and address the problem by normalizing layer inputs. Batch Normalization allows the researchers to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps and surpasses the original model by a significant margin.

Large-scale Video Classification with Convolutional Neural Networks

Author(s): Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei

Convolutional Neural Networks (CNNs) have been considered as a powerful class of models for image recognition problems. Encouraged by these results, the researchers provide an extensive empirical evaluation of CNNs on large-scale video classification. This used a new dataset of 1 million YouTube videos belonging to 487 classes. Provided by IEEE Conference on Computer Vision and Pattern Recognition, this research paper has been cited by 865 times with a HIC score of 24 and a CV of 239.

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Author(s): Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, Sameer Singh

Through this research paper around artificial intelligence, the authors point out the inadequacies of existing approaches to evaluating the performance of NLP models. The principles of behavioural testing in software engineering inspired researchers to introduce CheckList, a task-agnostic methodology for testing NLP models. It involves a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to produce a large and diverse number of test cases quickly.

Generative Adversarial Nets

Author(s): Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, SherjilOzair, Aaron Courville, YoshuaBengio

The authors in this AI research paper propose a new framework for estimating generative models via an adversarial process. They simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Author(s): Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

Advances like SPPnet and Fast R-CNN have minimized the running time of state-of-the-art detection networks, exposing region proposal computation as a bottleneck. To this context, the authors introduce a Region Proposal Network (RPN), a fully convolutional network that simultaneously predicts object bounds and abjectness scores at each position. RPN shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.

A Review on Multi-Label Learning Algorithms

Author(s): Min-Ling Zhang, Zhi-Hua Zhou

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. While there has been a significant amount of progress made toward the machine learning paradigm in the past decade, this paper aims to provide a timely review on this area with an emphasis on state-of-the-art multi-label learning algorithms.

Neural Machine Translation by Jointly Learning to Align and Translate

Author(s): DzmitryBahdanau, Kyunghyun Cho, YoshuaBengio

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belongs to a family of encoder-decoders. It involves an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation.

Mastering the game of Go with deep neural networks and tree search

Author(s): David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, and others

The paper introduces a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves in the game of Go. Go has been perceived as the most challenging of classic games for artificial intelligence. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.

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Last fall, Houston-based Mercury Data Science released an AI-driven app designed to help researchers unlock COVID-19-related information tucked into biomedical literature. The app simplified access to data about subjects like genes, proteins, drugs, and diseases.

Now, a year into the coronavirus pandemic, Mercury Data Science is applying this technology to areas like agricultural biotech, cancer therapeutics, and neuroscience. It’s an innovation that arose from the pandemic but that promises broader, long-lasting benefits.

Angela Holmes, chief operating officer of Mercury Data Science, says the platform relies on an AI concept known as natural language processing (NLP) to mine scientific literature and deliver real-time results to researchers.

“We developed this NLP platform as a publicly available app to enable scientists to efficiently discover biological relationships contained in COVID research publications,” Holmes says.

The platform:

• Contains dictionaries with synonyms to identify things like genes and proteins that may go by various names in scientific literature.
• Produces data visualizations of relationships among various biological functions.
• Summarizes the most important data points on a given topic from an array of publications.
• Depends on data architecture to automate how data is retrieved and processed.

In agricultural biotech, the platform enables researchers to sift through literature to dig up data about plant genetics, Holmes says. The lack of gene-naming standards in the world of plants complicates efforts to search data about plant genetics, she says.

The platform’s ability to easily ferret out information about plant genetics “allows companies seeking gene-editing targets to make crops more nutritious and more sustainable as the climate changes to have a rapid way to de-risk their genomic analyses by quickly assessing what is already known versus what is unknown,” Holmes says.

The platform allowed one of Mercury Data Science’s agricultural biotech customers to comb through scientific literature about plant genetics to support targeted gene editing in a bid to improve crop yields.

In the field of cancer therapeutics and other areas of pharmaceuticals, the platform helps prioritize drug candidates, Holmes says. One of Mercury Data Science’s customers used the platform to extract data from about 2 terabytes (or 2 trillion bytes) of information to evaluate drug candidates. The information included drug studies, clinical trials, and patents. Armed with that data, Mercury Data Science’s cancer therapy client signed agreements with new pharmaceutical partners.

The platform also applies to the hunt for biomarkers in neuroscience, including disorders such as depression, anxiety, autism and multiple sclerosis. Data delivered through the platform helps bring new neurobehavioral therapeutics to market, Holmes says.

“An NLP platform to automatically process newly published literature for more insight on the search for digital biomarkers represents a great opportunity to accelerate research in this area,” she says.

One of Mercury Data Science’s customers adopted the platform to improve insights into patients with depression and anxiety in order to improve treatment of those conditions.

The new platform — initially developed as a tool to combat COVID-19 — falls under the startup’s vast umbrella of artificial intelligence and data science. Founded in 2017, Mercury Data Science emerged because portfolio companies of the Houston-based Mercury Fund were seeking to get a better handle on AI and data science.

Last April, Angela Wilkins, founder, co-CEO and chief technology officer of Mercury Data Science, left the company to lead Rice University’s Ken Kennedy Institute. Dan Watkins, co-founder and managing director of the Mercury Fund, remains at Mercury Data Science as CEO.

The Ken Kennedy Institute fosters collaborations in computing and data. Wilkins replaced Jan Odegard as executive director of the institute. Odegard now is senior director of industry and academic partnerships at The Ion, the Rice-led innovation hub.

Wilkins “is an academic at heart with considerable experience working with faculty and students, and an entrepreneur who has helped build a successful technology company,” Lydia Kavraki, director of the Ken Kennedy Institute, said in a news release announcing Wilkins’ new role. “Over her career, Angela has worked on data and computing problems in a number of disciplines, including engineering, life sciences, health care, agriculture, policy, technology, and energy.”

According to a recently released report, a few key industries in Houston have attracted the bulk of the city’s venture capital investment dollars.

The Houston Tech Report by the Greater Houston Partnership and Houston Exponential has revealed that the city is home to 8,800 tech-related firms, including over 700 venture-backed startups that have attracted over $2.6 billion in VC funding over the past five years. Annual VC investment has tripled in that same timeframe — from $284 million in 2016 to $753 million in 2020.

“Houston is a city that has been leading the way for decades, with breakthrough innovations that have truly changed the world,” says Bob Harvey, president and CEO of the Greater Houston Partnership, in a news release. “Over the past few years, we have been working to transform an already incredible economy into one that competes as a leading digital tech city.”

Zooming into the industries attracting the most capital in Houston, life sciences and oil and gas technology continue to reign supreme. Of the VC dollars going into Houston companies, 17 percent goes into life science companies and 17 percent goes into oil and gas, according to the report. Cleantech and Oncology are both niches in Houston that have seen growth in VC investment.

Software as a service has seen significant growth since 2011, and represents the third-most invested in industry with 14 percent of the VC investment.

Contributing to the innovation ecosystem’s growth is an increase in startup development organizations — the city now has added over 30 SDOs including non-profits, incubators/accelerators, coworking spaces and makerspaces since 2017 — and access to tech talent. According to the report, Houston has the 12th largest tech sector in the U.S. with 235,000 tech workers, and this sector generates $28.1 billion to the region’s GDP.

“Houston in 2020 had not one but two unicorns (private tech companies exceeding a $1 billion valuation), our first ever,” says Harvin Moore, president of HX. “That’s a reflection of both the rate of growth and early stage of our ecosystem. We will see an increasing number of startups as these companies continue to grow and others follow.”

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For the fifth year in a row, Harvard, MIT, Microsoft Research New England, and now the Broad Institute, are proud to collaborate with Stanford University to bring the Women in Data Science (WiDS) conference to Cambridge, Massachusetts.

This virtual, one-day technical conference will feature an all-female lineup of speakers from academia and industry, to talk about the latest data science-related research in a number of domains, to learn how leading-edge companies are leveraging data science for success, and to connect with potential mentors, collaborators, and others in the field.

WiDS Cambridge is an independent event that is organized by Harvard, MIT, Microsoft Research New England, and the Broad Institute, as part of the annual WiDS Worldwide conference organized by Stanford University and an estimated 150+ locations worldwide, which features outstanding women doing outstanding work in the field of data science. All genders are invited to attend all WiDS Worldwide conference events.

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