Robotic technology will shape the world for better efficiency.

The Robotic Industry is changing the landscape of work in the modern era. Right from automation in every industry to autonomous surgeries in healthcare, robotics is now becoming capable of handling intricate responsibilities and repetitive tasks and perform them without errors. This is one of the reasons, industries are increasing their dependency on robotics. While this indicates the future of the industry, many organizations and startups are leveraging this demand to create innovative solutions.

Here are the robotic companies that received funding in the month of July to carry on their work and research.

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1. Realtime Robotics

Realtime Robotics is an autonomous industrial robotics company. The company offers real-time, collision-free motion planning that enables users to leverage robotic control technology to navigate applications. With just minimal programming, users can control the functioning of robots across multiple deployments. Realtime Robotics raised US$31.4 million in its Series A round. The funding was led by HAHN Automation for the purpose of improving robotic capabilities.

2. Verve Motion

Verve Motion pioneers connected wearable technology for the industrial sector. It accompanies robotics into functional apparel and boosts worker safety and wellbeing with their technology. The company recently announced that it has raised US$15 million in Series A funding which was led by Construct Capital along with Founder Collective, Pillar VC, Safar Partners, OUP, and other angel investors.

3. Seoul Robotics

Seoul Robotics is a 3D computer vision company that innovates intelligent robotic perception systems using artificial intelligence and machine learning. The company’s first commercial model, Discovery, made its debut in the US market, earlier this year. It is an all-in-one sensor and software platform that expands access, reduces costs, and simplifies the implementation of LiDAR-powered solutions in several applications for smart cities, smart, factories, and logistic mobility. Seoul Robotics was selected as an Innovative Icon startup for all its efforts and received a US$12 million investment from the Korean Government.

4. Fetch Robotics

Not so much of an investment, but a business firm, Zebra Technologies is planning to acquire Fetch Robotics, a cloud-driven autonomous mobile robot startup. This acquisition aims to boost Zebra Technologies Enterprise Asset Intelligence vision and growth in industrial automation. This will align with Zebra Technology’s focus on robotics and change the infrastructure of the industry with seamless integration.

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Big data is an area which has seen rapid growth across a variety of industries in recent years – not least among technology and communications companies.

Figures show that the number of new big data roles being advertised for these companies has increased in recent months with firms across the industry looking to expand their capabilities.

The number of newly advertised roles stood at 47,134 in Q1 2021. That’s up from 34,166 in Q4 2020 and up from 24,235 in Q3 2020.

The figures are compiled by GlobalData, who track the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.

GlobalData’s thematic approach to sector activity seeks to group key company information by topic to see which companies are best placed to weather the disruptions coming to their industries.

These key themes, which include big data, are chosen to cover “any issue that keeps a CEO awake at night”.

By tracking them across job advertisements it allows us to see which companies are leading the way on specific issues and which are dragging their heels – and importantly where the market is expanding and contracting.

Where are companies hiring for big data careers in technology?

Looking across key technology and communications companies tracked by GlobalData, the US is currently seeing the largest number of big data job advertisements. Last quarter the country saw 19,197 advertisements – up from 14,891 in Q4 2020 and 11,222 in Q3 2020.

On a city level, Bengaluru (India) had the most newly advertised big data roles in Q1 2021 with 2,074, followed by Hyderabad, (India) with 1,710, and Washington, (US) with 1,444.

Where is seeing the most growth for big data roles in technology?

The biggest growth area has been in India – which saw 4,624 job adverts for big data in Q4 2020, increasing to 10,972 in Q1 2021.

In terms of cities, firms are increasing big data hires in Chihuahua – with 41 roles in the latest quarter – up from zero in the previous one.

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With advanced software, these companies are making cognitive computing accessible.

Business applications of cognitive computing are gaining popularity rapidly. Cognitive computing technology combines machine learning, reasoning, NLP, speech vision, and human-computer interaction in a way that mimics the human brain to improve decision-making. This AI-powered capability has the potential to transform several industries, right from sales forecasting, improving communications, supply chain operations, to better drug discovery, marketing, defense, fraud detection, financial sector, and agriculture.

Tech companies that have released these applications are working on preparing products and services to help clients put data to better work.

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Innovative Cognitive Computing Companies

Aisera

Aesera’s AI Service Management Platform (AISM), helps customers and employees by optimizing processes for better productivity and slashed costs. The platform connects automated service experience with AI-based conversational engagement and workflow automation.

Accenture

Accenture aims to leverage all of its clients and their processes with the company’s unique approach to scaling AI, analytics, data, and automation. With applied intelligence, Accenture’s teams help organizations to invest in the right solutions and services that will suit their business goals.

AWS Machine Learning

AWS’s machine learning services and supporting cloud infrastructure, enable every developer, data scientist, and expert practitioner to use machine learning capabilities. At present, AWS is helping more than a thousand clients accelerate their machine learning capabilities.

Alteryx

Alteryx, provides a platform that facilitates end-to-end analytics process automation. The company recently announced new products that innovatively deal with analytics and data science automation, analytics in the cloud, AI, and machine learning. These new launches focus on delivering a simple user experience with no-code, low-code approaches to leverage business outcomes.

C3 AI

C3 AI, provides enterprise AI software that accelerates digital transformation with fully integrated products like C3 AI Suite (an end-to-end platform for AI applications), C3 AI Applications (a bundle of industry-specific SaaS AI apps), C3 AI CRM (CRM applications for AI and ML), and C3 AI Machina, a no-code AI solution for everyday data science.

SparkCognition

SparkCognition, provides three cognitive computing software for enterprises, SparkPredict, SparkSecure, and MindFabric. SparkPredict uses sophisticated algorithms to large pools of data with intelligence. SparkSecure Cognitive Insights add a cognitive layer to security solutions to improve threat detection, leverage IT abilities, and reduce the probability of false positives. MindFabric platform acts as a workspace for professionals for deep data-led insights.

Microsoft Cognitive Services

Microsoft’s Cognitive Services boosts Microsoft’s machine learning APIs to help developers easily add intelligent features like emotion detection, voice recognition, and language understanding. With just a few lines of code, developers can build apps that can work across devices like iOS, Android, and Windows.

Expert System

Expert System, provides software that is capable of working with language and technology to make sense out of unstructured content. Clients can extract insights and make human-level decisions with strengthened analytics. This software comprehends multiple languages, just like humans.

IBM Watson

IBM Watson performs deep content analysis and uses evidence-based reasoning to leverage and improve decision making, reducing costs for better outcomes. For this, the software uses

A set of transformational technologies that use natural language, hypothesis generation, and evidence-based learning. Experts believe that Watson holds the power to transform the process of business problem solving as the system uses machine learning, statistical analysis, and NLP to find answers amidst the clues. Watson then compares the answers by ranking them based on confidence and accuracy.

Deepmind

Deepmind aims to solve intelligence-based business problems with the research. Deepmind uses real-world applications of AI technology to help industries like healthcare. It enables nurses, doctors, and support staff to quickly analyze test results, forms the right diagnosis and treatment, and escalate the case to a specialist. All these judgments can be made using the advanced technology of accurate analysis.

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The hype for highest paying companies for Data Scientists attracts more Aspirants

The data science landscape is filled with opportunities spanning diverse industries. As new technologies are being added to the digital sphere year-on-year, the transformation is likely to continue into the coming decade. Owing to the increasing influence of technology in our daily lives, the demand for data science jobs has drastically spiked. The openings for data scientists are expected to go beyond 2021, adding more than 150,000 jobs in the coming years. This trend is a natural response of the digital age for adding more data into its ecosystem. Besides paying high salaries, data science jobs are demanding when it comes to talent requirements and innovation. Data science requires the expertise of professionals, who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. On a positive note, the nature of data science jobs allows an individual to take on flexible remote works and also to be self-employed. Despite the leniency, the hype for highest paying companies for data scientists remains at the top. In this article, Analytics Insight has listed the top 10 companies that are paying a fortune for data scientists in 2021.

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Top companies paying high salaries to data scientists

Oracle

Data Scientist’s salary: US$124,333

Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system that’s formally called Oracle Database. In 1979, Oracle became the first company to commercialize an RDBMS platform. The enterprise software company offers a range of cloud-based applications and platforms as well as hardware and services to help companies improve their processes. Oracle recently announced the availability of its cloud data science platform, a native service on Oracle Cloud Infrastructure (OCI).

Pinterest

Data Scientist’s salary: US$162,931

Pinterest is a social sharing website where individuals and businesses can ‘pin’ images on ‘boards’ in order to share visual content with friends and followers. Today, many businesses are using interest as a source to enhance their business by promoting content in it. Pinterest creates a lot of online referral traffic so it’s great for attracting attention. Pinterest has a special data science lab where its leading data scientists work to accelerate the company’s development. So far, the data science team has created a systematic approach to data science, which gives them trustworthy conclusions that are both reproducible and automatable.

Lyft

Data Scientist’s salary: US$157,798

Lyft is an online ridesharing provider that offers ride booking, payment processing, and car transport services to customers in the United States. Introduced in 2012, Lyft leverages a friendly, safe, and affordable transportation option that fills empty seats in passenger vehicles already on the road by matching drivers and riders via a smartphone application. Owing to its need for data science professionals, Lyft has so far assembled a team of over 200+ data scientists with a variety of backgrounds, interests, and expertise.

Uber

Data Scientist’s salary: US$146,032

Uber is also a transportation company, well-known for its ride-hailing taxi app. The company has since become synonymous with disruptive technology, with the taxi app has swept the world, transforming transportation and giving a different business model, dubbed uberisation. Founded in 2009, the app automatically figures out the navigational route for drivers, calculates the distance and fare, and transfers the payment to the driver from users’ selected payment method. Therefore, data science is an internal part of Uber’s products and philosophy.

Walmart

Data Scientist’s salary: US$137,668

Walmart is one of the biggest retailers in the world started by Sam Walton. The company sells groceries and general merchandise, operating some 5,400 stores in the US, including about 4,800 Walmart stores and 600 Sam’s Club membership-only warehouses. Through continuous innovation and the implication of technology, the company has created a seamless experience to let its customers shop anytime and anywhere online and offline. Walmart has a broad big data ecosystem that attracts more data scientists into the entity.

Nvidia

Data Scientist’s salary: US$197,500

Nvidia is an artificial intelligence computing company that operates through two segments namely graphics and compute & networking. Nvidia is known as a market leader in the design of graphics processing units, or GPUs, for the gaming market, as well as systems on chips, or SOCs, for the mobile computing and automotive markets. Nvidia works on the motive that accelerated data science can dramatically boost the performance of end-to-end analytics workflows, spending up value generation while reducing cost.

Airbnb

Data Scientist’s salary: US$197,800

Airbnb takes a unique approach towards lodging by providing a shared economy. The platform offers someone’s home as a place to stay instead of a hotel. Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Today, millions of hosts and travelers choose to create an Airbnb account so they can list their space for rentals. The company is using data science to build new product offerings, improve its services, and capitalize on new marketing initiatives.

Netflix

Data Scientist’s salary: US$173,503

Netflix is a streaming entertainment service company, which provides subscription services streaming movies and television episodes over the internet and sending DVDs by mail. For millions, Netflix is a de facto place to go for movies and series. Netflix was founded in 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. Data science plays an important role in the Netflix routine. With the help of data science, the company gets a more realistic picture of its customers’ taste in form of graphs and charts. It eventually helps the platform’s recommendation service.

Dropbox

Data Scientist’s salary: US$145,172

Dropbox is a cloud storage service company that lets users save files online and sync them to their devices. Dropbox is one of the oldest and most popular cloud storage services that has strongly outperformed Microsoft’s OneDrive and Google Drive. Founded in 2007, the company offers a browser service, toolbars, and apps to upload, share, and sync files to the cloud that can be accessed across several devices.

Genentech

Data Scientist’s salary: US$129,833

Genentech is a biotechnology company that discovers, develops, manufactures, and commercializes medicines to treat patients. The company offers medicine for the prevention of oncology, immunology, metabolism, monoclonal antibodies, small molecules, tissue repair, and virology, as well as conducts scientific research to produce biologic medicines. The company uses its data science capabilities to enhance its performance in the market by unraveling effective medicines.

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These Artificial Intelligence Companies, Now have the Funding to Innovate

The rate at which artificial intelligence is advancing is shaping the world around us. From automation to drug development and diagnosis, there is no industry that AI has not taken over. Like the silver lining to a dark cloud, the chaos created by COVID-19 pandemic has made organizations realize the power of artificial intelligence and are gearing up to invest heavily. Many renowned investors and Fortune 500 companies are also backing up budding AI companies that show promising growth potential. For the month of June in 2021, these are the AI companies that received heavy funding. For information on AI investments in April and May, check out this link.  

Top AI funding in June

CoffeeMug.ai

Transaction type: Pre-seed

Key investor(s): Paradigm Shift Capital and AngelList India

Singapore based AI professional networking platform, CoffeeMug.ai has raised US$625,000 in pre-seed funding round. Govind Mundhra, Founding General Partner of Paradigm Shift Capital, one of the key investors, will be joining the board of directors of CoffeeMug.ai. As per official reports, CoffeeMug will use these funds to accelerate its efforts to improve its AI-based matchmaking algorithm, double its user base and connect them with leadership level business opportunities.

Upside AI

Transaction type: Seed money

Key investor: Endiya Partners

Mumbai-based investment management startup, Upside AI, raised US$1.2 million in seed funding. Led by venture capital fund Endiya Partners, Upside AI plans to use the funds to fill up leadership positions and other senior roles. According to its cofounder Atanuu Agarrwal, the company intends to add 1,000 high net worth individuals, family offices, and institutional clients.

Eightfold AI

Transaction type: Series E

Key investors: SoftBank Vision

Eightfold AI is a talent-based intelligence platform provider based out of California. In the Series E funding round led by SoftBank Vision, with participation from General Catalyst, Capital One Ventures, Foundation Capital, IVP, and Lightspeed Venture Partners, Eightfold AI raised US$220 million. The company intends to use the total funding to promote further growth and development, and expand its partner ecosystem.

Gloat

Transaction type: Series C
Key investor(s): Accel, Eight Roads Ventures, Intel Capital, Magma, PICO

Gloat is an AI-enabled talent marketplace platform. Headquartered in New York, Gloat has expanded its offices globally, with an R&D center in Tel Aviv. The company assists companies in connecting their employees with development opportunities like projects and mentorship programs. In a Series C funding round co-led by Accel, Eight Roads Ventures, Intel Capital, Magma Venture Partners, and PICO partners, Gloat raised US$57 million which will be used to accelerate product innovation.

Cleerly

Transaction type: Series B

Key investor: Vensana Capital

Cleerly is a New York City-based company, that uses AI and machine learning algorithms to diagnose coronary artery disease. The company has developed a software platform that facilitates whole-heart quantification and characterization of atherosclerosis. Unlike traditional techniques that take hours, Cleerly’s software can perform the diagnosis in a matter of minutes, noninvasively. Led by Vensana Capital, Cleerly raised US$43 million in Series B funding, which will be used to scale its commercial reach, expand capabilities, strategic partnerships, and focus on R&D.

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Artificial intelligence is playing a vital role in processing the big data sets of industrial companies.

Artificial intelligence is being used by large industrial companies to analyze their long array of unstructured datasets and put them into smart use. AI is creating analytics models that are creating accurate operating strategies based on variables like pump speed or weather. To be successful in this process, the big industries must know how to create an amenable environment for AI to work properly with their big datasets.

The Making of Smart Data

There is a five-step approach that can be adapted to process the big datasets into smart data. First of all, the steps of the process must be outlined, along with addressing the physical and chemical changes like grinding, heating, oxidation, and polymerization. The process flow of the operation should be labeled using paint schematics or engineering drawing. In the next step, the non-standard operating regimes should be removed. A common data science approach should be used to engineer input combinations to produce new features. When combined with the sheer number of sensors available in modern plants, this demands a massive number of observations. Instead, teams should prepare the features list to include only those inputs that describe the physical process, and then they should apply deterministic equations to create features that intelligently combine sensor information.

The sensor calibrations should be addressed and a high-quality dataset should be built. The next phase of the process would be to leverage the engineering formulas to combine the sensor data in an intelligent manner.

In the next step, advanced analytic models should be overlaid on engineered data for capturing the stochastic variability. Teams should evaluate features by inspecting their importance and therefore their explanatory power. Ideally, expert-engineered features that capture, for example, the physics of the process should rank among the most important. Overall, the focus should be on creating models that drive plant improvement, as opposed to tuning a model to achieve the highest predictive accuracy. Teams should bear in mind that process data naturally exhibit high correlations. In some cases, model performance can appear excellent, but it is more important to isolate the causal components and controllable variables than to solely rely on correlations. The last step includes checking casualties and ensuring the facts that the results are physical.

The Making of Analytics Team

The team responsible for the implementation of AI must have a variety of members from operators to data scientists, automation engineers, and process experts. Companies that are looking to implement AI generally need to rebuild their expert pipeline initially. Knowing the skills is the most important factor when it comes to choosing the perfect process expert. Planning out the model development can be a good exercise to solidify a way of working and avoid linear approaches that include exhaustively completing one stage before proceeding to the next. Later the team can decide what to invest in for the next stage.

Industrial companies are looking to AI to boost their plant operations, reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations. The cross-functional data science teams should include employees who are capable of bridging the gap between machine learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.

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Since the 1950s, scientists have been actively studying the capabilities of computer intelligence. Over the last 70 years, the concept of machine learning (ML) has developed from a theory to a technology actively used in the wild that allows program autonomy for decision-making, reducing the amount of manual work needed.

Security pros use machine learning to boost and automate malware detection, among other things. We also use machine learning against advanced email phishing. Sophisticated, accurately prepared phishing letters are effective ways to trick a specific organization or a user. Attackers disguise these messages as emails from new online services, exploiting popular events, even the pandemic. In the first quarter of 2020, there were many emails circulated with a request to transfer money to help combat COVID-19. Through the business email compromise technique, criminals gain employees’ trust via email correspondence. They disguise themselves as a third party, contractor or even a colleague and make targets do what the criminals want.

To protect users from such attacks, a security solution should quickly analyze all parameters of the email, including the content and technical characteristics, to detect if it’s malicious or not. Machine learning can take care of this.

In this case, we should use two machine learning models. One model will automatically analyze the technical parameters of emails, such as technical headers. It references hundreds of millions of metadata records from real emails and learns to recognize the combinations of technical traces that prove that the email is malicious, but it’s not enough information to make a verdict.

The second model detects the malicious nature of an email based on its content. To achieve the desired emotional effect, attackers use emotive language, as well as a clear call to action, something like: “your parcel couldn’t be delivered, update your data here” in their text. The model recognizes such words and phrases typical for phishing letters.

The two models then correlate both results and make the final decision of whether or not it’s a phishing email, saving the user from opening it.

Machine learning against mobile threats for Android

In 2020, Kaspersky researchers detected an increase of 2 million more mobile threats than in the previous year, totaling more than 5 million overall. One of the key tasks within mobile protection is to secure against unknown malicious objects which have recently appeared in the wild.

On iOS devices, it’s only possible to install apps for a wide audience from the Apple App Store. On Android devices, users can install apps from a variety of sources and app markets. Unfortunately, cybercriminals sometimes exploit this by posting malware in apps disguised as games, useful software, porn, and so on. To detect the threats effectively and quickly, security teams need machine learning.

A machine learning agent on a user’s device scans every app as it’s downloaded for specific features such as required access permissions or numbers and sizes of internal structures. The metadata gets sent to the cloud-based ML model that then decides if this set of parameters causes the app to be classified as malicious or not. The model then sends a response indicating whether it’s a malicious file or not, and the protection product on the device decides to block the app’s download and installation.

This type of machine learning analysis requires a lot of computing resources, much more than a mobile device has available, and that’s why security teams leverage the cloud for this process. 

Machine learning can prevent plant breakdowns

Equipment malfunctions, misconfigurations, human error, or hacker attacks can all cause the breakdown of industrial machinery. If any of these situations happen, it’s better to detect the deviation in production processes as soon as possible, otherwise an incident can spiral out of control.

The early symptoms of an incident are virtually impossible to detect by threshold monitoring or human operators. When thousands of telemetry readings come in every second, even an experienced operator can focus on a few patterns and overlook the rest.

Here’s where machine learning for anomaly detection (MLAD) comes in. The neural network can analyze a massive amount of telemetry data, absorb all aspects of the machine’s operation and thoroughly learn how the machine behaves under the normal conditions such as how the signals change over time and how they correlate with each other.

Once the ML model gets trained, the model switches to anomaly detection mode. It then receives telemetry in real-time, and if the divergence between the model and the observation rises above a certain threshold, the software deems the machine’s behavior anomalous and raises an alarm. The model gives an early warning of attacks, malfunctions, or mismanagement before any other instrument can spot the problem. This way, it helps to minimize damage and prevent a plant’s breakdown.

Machine learning against advanced cyberattacks

In some cases, security teams can use machine learning techniques to complement human intelligence against advanced threats, such as in managed detection and response (MDR) services.

Within an MDR service, an external security operation center (SOC) helps business customers respond to advanced cyberattacks. It receives alerts from the customer’s endpoints and investigates them to find traces of attacks, which it then reports back to the customer with response actions. SOC experts analyze some threat samples manually, but given the scale, they physically cannot look at each and every alert.

Machine learning can take on this burden. It automatically filters out alerts of no interest for SOC analysts, sets alert importance levels and gives hints for analysis saving their capacity and minimizing the response times.

During the training mode, model analyses alerts and scores them. The higher the score, the greater the probability that the alert should get reviewed by experts. Alerts with scores above a certain threshold are sent to SOC analysts who label them manually and enrich training data for the ML model.

In combat mode, the model resolves some alerts and prioritizes the rest for manual processing: Those with the highest score are put at the head of the queue for processing. This queue strategy reduces the average processing time of alerts and allows the offering to deliver the best SLA.

These are just a few interesting cases of how machine learning serves cybersecurity goals, but we believe that the field will continue to expand. Developing ML techniques in products can make cyber protection more intelligent, faster, and efficient.

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There is a tremendous demand for voice AI all over the world, especially in the Indian market. In the post-pandemic world, the need for machines with touch-less UI is growing, and the voice AI comes across as the perfect solution, says Tapan Barman, Co-founder and CEO, Mihup

Voice AI technology is no longer a stranger to most of us, especially those with love for technology. Alexa manages devices in many homes today, and every single smartphone

has either Google Assistant or Siri waiting for the user’s commands. The advancement of voice AI has reached a level where we can talk to machines around us just the way we speak to another person. Many car companies are already offering embedded voice Assistants and others are set to follow the lead soon.

Research and market report pegged the current voice AI market at around US$ 100 billion and growing 15 percent YoY. There is a tremendous demand for voice AI all over the world, especially in the Indian market. In the post-pandemic world, the need for machines with touch-less UI is growing, and the voice AI comes across as the perfect solution. Many internet users are already using voice AI Assistants on their phones and devices to search for information, entertainment, music and commerce. As per a study by Adobe, 71 percent of American smart home speaker owners Claimed to use it daily while more than half used the speakers several times a day. According to brightlocal, 46 percent of voice AI users search for local businesses daily. In the digital world, the mantra for any business’s success is to meet the consumer where they want and when they want. That’s where companies that don’t integrate voice Assistants into their processes are likely to fall behind in the future.

At the backend, the AI-based conversational tools are software applications developed to automate natural language conversations. A company can use them as chat or voice assistants. A modern conversational AI system receives the user’s query through either written or spoken input. Then it offers the desired information with speed and accuracy similar to a regular conversation between humans. The existing tools are rooted in two major aspects: speech recognition and knowledge modelling.

Any voice AI Assistant’s efficiency, and accuracy will depend on its ability to comprehend the voice input correctly. This is where the first-generation voice AI tools had struggled as the American companies mostly produced them to understand the specific American accent. However, the modern voice assistants are becoming rapidly proficient in natural language processing as start-ups outside the US focus on creating phonetic based voice AI tools instead of those that rely on identifying words. The reliance on natural language processing and AI reduces the inaccuracies and empowers the voice assistants globally to deliver a more incredible experience to users worldwide. The market potential is huge, and with one of the largest populations on the planet, India offers great potential for companies using voice assistants.

There are many reasons why voice AI will be the future for business operations in the post-pandemic world. As we see already with many large companies, voice AI enables them to deploy automated round-the-clock customer service. The visitor engagement and traffic on websites and apps using such tools are usually higher as they can get relevant information in near real-time compared to the cumbersome process of contacting the sales team and seeking a response for their queries.

Voice AI is also playing a significant role in making customer service more efficient. They are taking up regular business communication and everyday customer service operations to free up precious human resources and focus on advanced tasks. When it comes to personalization and intuitive customer experience, the AI tools deliver excellent service in verticals such as sales, marketing, HR and others. Since the voice AI and chatbots store their conversations with the consumers, they also facilitate great insights into consumer behavior and expectations and improve their user experience. They can also generate leads and create products and services that are more likely to appeal to their consumers. While usage of consumer data to analyze and extract insights for the business is nothing new, the voice AI has transformational abilities in this arena. The analysis of speech-based data goes beyond typical data. It provides consumer feedback, but the tone, language patterns, and acoustics enable the systems to pick up consumer sentiments. Whether someone is agitated or happy, complaining or making suggestions or sharing positive feedback, it is acknowledged with greater clarity and impact with interaction analytics.

Thus, it is imperative for any company that envisions itself as a leading brand of the future to plan using AI voice assistant technology in its operations soon!

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The list represents top companies hiring data science professionals in Switzerland in March 2020

The world is powered by exponential data. With structured and unstructured data growing dramatically in companies’ data centers, organizations are looking for professionals who can work on sorting the data and make insightful decisions from it. This has led to a spike in the demand for data science professionals. The data science field is drastically evolving and the need to fill data science jobs is also growing. It is no different in Switzerland. Data science in Switzerland is also ballooning and the leading tech players are looking to hire data science professionals. Data scientists manage, mine, and clean unstructured data and prepare it for practical use. It is later used to deliver results that impact business outcomes. Some of the common data science jobs in Switzerland include data scientist, data analyst, data engineer, enterprise data architect, research scientist, etc. Analytics Insight has listed top companies hiring data science professionals in Switzerland in March 2020.

Data Scientist at Tata Consultancy Services

Tata Consultancy Services Ltd is an IT services, business solutions, and outsourcing organization that delivers real results to global businesses, ensuring a firm level of certainty. Founded in 1968 and initially based in India, the company now has a global presence, deep domain expertise in multiple industry verticals, and a rich portfolio of services such as consulting and service integration, digital transformation services, and cognitive business operations.

Job role and responsibilities: As a data scientist, the candidate should support the analysis of the data in Risk and Compliance area. He/she should prepare additional big data and data analysis technologies within the department, like data modelling, wrangling, pipelines implementation, etc. The candidate should build machine learning models on Palantir Foundry stack. He/she should review and methodologies to match clients via name matching.

Research Scientist at Microsoft

Location: Zurich, Switzerland

Microsoft Corporation is an American multinational technology company headquartered in Redmond, Washington. Microsoft is the leading developer of personal computer software systems and applications. The company also publishes books and multimedia titles, produces its own line of hybrid tablet computers, offers email services, and sells electronic game systems and computer peripherals. Founded in 1974, Microsoft is now well established across the globe.

Job role and responsibilities: The research scientist will work with an interdisciplinary team from conducting state of the art research to developing early prototypes to productization of technologies for social presence and action understanding in mixed reality. He/she should engage with the research community through publications, workshops or the supervision of interns & PhD students. The company demands the candidate to be self-motivated, strong as a team-player, should be a good communicator, and must accept challenges.

Data scientist at eBay

Location: Zurich, Switzerland

eBay Inc. is a global commerce leader that connects millions of buyers and sellers around the world. Founded in 1995, the company enables the economic opportunity for individuals, entrepreneurs, businesses, and organizations of all size. eBay portfolio of brands includes eBay Marketplace and eBay Classifieds Group, operating in 190 markets across the globe.

Job role and responsibilities: As the role is data-intensive, the data scientist is expected to be comfortable working with large sets of data and deliver business insights from the data using analytical techniques. He/she should be able to offer a strategic perspective, sound business judgement, and willing to work collaboratively with multiple teams in different geographies. The potential candidate should have a hands-on approach to data analysis, machine learning, strong intellectual curiosity, and a passion for achieving practical business impact.

Research scientist at DFINITY

Location: Zurich, Switzerland

The DFINITY Foundation is a not-for-profit organization based in Zurich, Switzerland, that oversees research centers in Palo Alto, San Francisco, Tokyo, and Zurich. The foundation’s mission is to build, promote, and maintain the internet computer and improve the world by doing so. The internet computer extends the functionality of internet from connecting billions of people to also providing million of developers and entrepreneurs with a public compute platform, creating a revolutionary new way to build websites, enterprise systems and internet services within an open environment.

Job role and responsibilities: As a research scientist, the potential candidate should collaborate with engineers to develop secure, reliable and scalable solutions, from problem identification and solution conception of production. He/she should provide deep technical insight in a field of interest and communicate it clearly. The candidate should contribute to designs and specifications of the next iterations of the Internet Computer.

Data engineer at SystemsAccountants

Location: Zurich, Switzerland

SystemsAccountants is a highly specialist recruitment and resourcing solutions company, focused exclusively on financial, accounting technology and transformation. The company specializes solely in sourcing, vetting and placing professionals who have a hybrid combination of financial/accounting experience and qualifications, blended with technology, project and change management expertise.

Job role and responsibilities: As a data engineer, the potential candidate will join fellow data enthusiasts, specializing in data engineering and integration. The team is responsible for integrating a wide range of Information Systems and helping make the best data available to the Data Insight team. The role involves significant projects while also managing support of existing ETL and Data pipelines.

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A look at the best machine learning companies that are profitable for your business

Machine learning is an advanced artificial intelligence technology that can learn from data using algorithms and then make predictions. The growth of machine learning in recent years has been significantly driven by a boundless quantity of data, affordable data storage, and the evolution of more powerful processing. As organizations are increasingly stepping to leverage robust ML models, finding the best machine learning companies that deliver all the features is quite difficult.

To avoid this hunt for your business, Analytics Insight has listed the top 10 machine learning companies that are worth looking at in 2021.

Iflexion

Founded in 1999, Iflexion delivers software development and related IT services. The Austin, Texas-based company provides full-cycle services in the areas of content management solutions, portals, e-commerce, web-based enterprise solutions, media content distribution, social software. Iflexion has built a custom online payment solution, a supporting CRM, a payroll system, and a standalone mobile app for a financial tech company.

iTechArt Group

iTechArt is a top-tier, one-stop custom software development company offering software and application development, modernization, migration, QA & testing services. Founded in 2002 and headquartered in New York, the company does not only make software AI-enabled or blockchainable, but makes it work for companies’ business goals and target market.

Talentica

Talentica is a Pune, Maharashtra, India-based innovative outsourced product development company. It helps small and mid-sized technology companies take advantage of global talent to reduce time to market and R&D costs. The company engineers practical data-driven algorithms to power machine intelligence for startups by separating the AI hype from computational realities.

UruIT

UruIT is a nearshore software development company. It is developing a new way of creating and extracting metadata across the enterprise. UruIT delivers machine learning capability for searching, identifying and organizing data in the entertainment industry. The company’s major clients include Disney, Warner, BBC, FOX News, and HBO. Huge players in the market rely on this platform to search across multiple sources of media content faster and better by using Machine Learning.

DeCypher DataLabs LLC

DeCypher DataLabs LLC, is a Chicago-based boutique business practice that specializes in all Advanced Analytics and Machine Learning matters for US government agencies, commercial enterprises, and non-profit organizations. The company provides AI solutions that are hosted on the AWS platform and integrate AWS into custom-developed solutions. DeCypher DataLabs guiding principles are based on Truth and Data Ethics.

MobiDev

MobiDev is a Georgia, United States-based software development company. It creates complex business-driven solutions, with a focus on innovation and transparency of actions, guaranteed product delivery, and ongoing evolution. The company’s main areas of expertise include Industrial IoT and Augmented Reality, Data Science & Machine Learning, Blockchain & distributed databases, Microservices & cloud infrastructure, Native mobile & desktop development, and Cross-platform solutions.

Neoteric

Neoteric is a Poland-based end-to-end software product development company helping startups and enterprises. The company’s services include Web app development, AI-driven solutions, SaaS app development, Product design, and RPA. Neoteric supports companies with end-to-end software development leading the tech from product idea, through prototyping to launch.

Belitsoft

Outsourcing software development company Belitsoft is committed to providing high-quality software products and services. The company focuses on web portals and applications, e-commerce, and more. Based in Minsk, Belarus, Belitsoft provides a wide variety of services. These include custom software development, automated software testing, maintenance & support, quality assurance and more.

Netguru

Poland-based custom software development company Netguru builds digital products that allow people do things differently. The company provides machine learning solutions to businesses to gain a competitive advantage. Netflix saves US$1 billion each year thanks to machine learning algorithms. Its recommendation engine uses dozens of algorithms to compare viewers’ preferences with similar customers around the world.

Unicsoft

Unicsoft is a full-cycle custom software development delivering AI and Blockchain solutions to drive business outcomes for startups and enterprises. The company has delivered more than 200 projects incorporating Blockchain and AI, IoT and Machine Learning, Web and Mobile Development. Unicsoft also delivers its day-to-day services with transparency, clarity, and most importantly, care for its customers.

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