Definition: The terms "robotics in snow and ice" refers to robotic systems being studied, developed, and used in areas where water can be found in its solid state. This specialized branch of field robotics investigates the impact of extreme conditions related to cold environments on autonomous vehicles.

Using GitHub for data science research is not quite mainstream just yet for the data science industry, but it is growing in popularity in the field. From our point of view, there is a good reason for that growth. Data science has been a siloed field of research, but as technology continues to develop so does data science. It is now more common to see teams of data scientists all working towards similar goals rather than a single data scientist sifting through data on their own and pushing it forward to a team of developers or engineers. In this guide, we will explain how data science and GitHub are a perfect match, but also the "why" behind using GitHub at all for data science.

We have reached an inflection point. After AWS pioneered cloud technology more than 15 years ago, cloud infrastructure has evolved to a place where we are seeing all parts of the cloud reach practically anywhere on the planet--and even into space. The cloud has allowed what was once science fiction to become science fact. Models and techniques in the artificial intelligence (AI) and machine learning (ML) realm have gotten better and better--so much so that we see glimpses of new kinds of use cases emerging that we previously only imagined in movies and comics. We are entering a phase where data is abundant, access to it is almost instantaneous, and our ability to make sense of it in new and subtle ways is practically automatic.

The accelerating pace of global change is driving a biodiversity extinction crisis ([ 1 ][1]) and is outstripping our ability to track, monitor, and understand ecosystems, which is traditionally the job of ecologists. Ecological research is an intensive, field-based enterprise that relies on the skills of trained observers. This process is both time-consuming and expensive, thus limiting the resolution and extent of our knowledge of the natural world. Although technology will never replace the intuition and breadth of skills of the experienced naturalist ([ 2 ][2]), ecologists cannot ignore the potential to greatly expand the scale of our studies through automation. The capacity to automate biodiversity sampling is being driven by three ongoing technological developments: the commoditization of small, low-power computing devices; advances in wireless communications; and an explosion in automated data-recognition algorithms in the field of machine learning. Automated data collection and machine learning are set to revolutionize in situ studies of natural systems. Automation has swept across all human endeavors over recent decades, and science is no exception. The extent of ecological observation has traditionally been limited by the costs of manual data collection. We envision a future in which data from field studies are augmented with continuous, fine-scale, remotely sensed data recording the presence, behavior, and other properties of individual organisms. As automation drives down costs of these networks, there will not be a simple expansion of the quantity of data. Rather, the potential high resolution and broad extent of these data will lead to qualitatively new findings and will result in new discoveries about the natural world that will enable ecologists to better predict and manage changing ecosystems ([ 3 ][3]). This will be especially true as different types of sensing networks, including mobile elements such as drones, are connected together to provide a rich, multidimensional view of nature. Given the role that biodiversity plays in lending resilience to the ecosystems on which humans depend ([ 4 ][4]), monitoring the distribution and abundance of species along with climate and other variables is a critical need in developing ecological hypotheses and for adapting to emerging global challenges. Ecosystems are alive with sound and motion that can be captured with audio and video sensors. Rapid advances in audio and video classification algorithms can allow the recognition of species and labeling of complex traits and behaviors, which were traditionally the domain of manual species identification by experts. The major advance has been the discovery of deep convolutional neural networks ([ 5 ][5]). These algorithms extract fundamental aspects of contrast and shape in a manner analogous to how we and other animals recognize objects in our visual field. Applied to audio signals, these neural networks are highly effective at classifying natural and anthropogenic sounds ([ 6 ][6]). A canonical example is the classification of bird songs. Other acoustic examples include insects, amphibians, and disturbance indicators such as chainsaws. Naturally, these algorithms also lend themselves to species identification from images and videos. In cases of animals displaying complex color patterns, individuals may be distinguished, allowing minimally invasive mark recapture, an important tool in population studies and conservation ([ 7 ][7]). Beyond sight and sound, sensors can target a wide range of physical, chemical, and biological phenomena. Particularly intriguing is the possibility for widespread environmental sensing of biomolecular compounds that could, for example, allow quantification of “DNA-scapes” by means of laboratory-on-a-chip–type sensors ([ 8 ][8]). Several technological trends are shaping the emergence of large-scale sensor networks. One is the ongoing miniaturization of technology, allowing deployment of extended arrays of low-power sensor devices across landscapes [for example, ([ 9 ][9])]. In many cases, these can be solar-powered in remote locations. The widespread availability of computer-on-a-chip devices along with various attached sensors is enabling the construction of large distributed sensing networks at price points that were formerly unattainable. Similarly, the ubiquitous availability of cloud-based computing and storage for back-end processing is facilitating large-scale deployments. Another trend is advancements in wireless communications. For example, the emerging internet of things ([ 10 ][10]) enables low-power devices to establish ad hoc mesh networks that can pass information from node to node, eventually reaching points of aggregation and analysis. The same technology used to connect smart doorbells and lightbulbs can be leveraged to move data across sensor networks distributed across a landscape. These protocols are designed for low power consumption but may not have sufficient bandwidth for all applications. An alternative, although more power hungry, is cellular technology, which has increasing coverage globally. In remote locations, where commercial cellular data services may not be available, researchers can consider a private cellular network for on-site telemetry and satellite uplinks for internet streaming. However, in the near term, telecommunications costs and per-device power requirements may nonetheless prove prohibitive in certain high-bandwidth applications, such as video and audio streaming. An alternative for sites where communications bandwidth is limited by cost, isolation, or power constraints is edge computing ([ 11 ][11]). In this design, computation is moved to the sensing devices themselves, which then transmit filtered or classified results for analysis, greatly reducing transmission requirements. One more trend is the advancement of machine-learning methods ([ 12 ][12]) that can classify and extract patterns from data streams. Much of this technology has been commoditized through intensive development efforts in the technology sector that have resulted in widely available software libraries usable by nonexperts. The aforementioned convolutional neural networks can be coded both to segment data into units and to label these units with appropriate classes. The major bottleneck is in training classifiers because initial training inputs must be labeled manually by experts. Although labeled training sets exist in some domains—most notably, image recognition—future analysts may be able to skip much of the training step as large collections of pretrained networks become available. These pretrained networks can be combined and modified for specific tasks without the requirement of comprehensive training sets. Of particular interest from the standpoint of automation are new developments in continual learning ([ 13 ][13]), in which networks adjust in response to changing inputs. This holds the promise of automating model adaptation for detecting emerging phenomena, such as species shifting their ranges in response to climate change or other shifts in ecosystem properties. Ecologists could leverage these developments to create automated sensing networks at scales previously unimaginable. As an example, consider the North American Breeding Bird Survey, a highly successful citizen-science initiative running since the late 1960s with continental-scale coverage. Expert observers conduct point counts of birds along routes, generating data that have proved invaluable in tracking trends in songbird populations ([ 14 ][14]). Although we hope to see such efforts continue, imagine what could be learned if, instead of sampling these communities once per year, a long-term, continental-scale songbird observatory could be constructed to record and classify bird vocalizations in near–real time along with environmental covariates. Similar networks could use camera traps or video streams to reveal details of diurnal and seasonal variation across diverse floras and faunas. As with all sampling methods, sensing networks will not be without biases in sensitivity and discrimination, yet they hold the extraordinary promise of regional sampling of biodiversity at the organismal scale, something that has proven difficult, for example, by using traditional satellite-based remote sensing. These efforts would complement ongoing development of continental-scale observatories in ecology [for example, ([ 15 ][15])] by increasing the spatial and temporal resolution of sampling. 1. [↵][16]1. S. Díaz et al ., Science 366, eaax3100 (2019). [OpenUrl][17][Abstract/FREE Full Text][18] 2. [↵][19]1. J. Travis , Am. Nat. 196, 1 (2020). [OpenUrl][20] 3. [↵][21]1. M. C. Dietze et al ., Proc. Natl. Acad. Sci. U.S.A. 115, 1424 (2018). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. B. J. Cardinale et al ., Nature 486, 59 (2012). [OpenUrl][25][CrossRef][26][PubMed][27][Web of Science][28] 5. [↵][29]1. Y. LeCun, 2. Y. Bengio, 3. G. Hinton , Nature 521, 436 (2015). [OpenUrl][30][CrossRef][31][PubMed][32] 6. [↵][33]1. S. S. Sethi et al ., Proc. Natl. Acad. Sci. U.S.A. 117, 17049 (2020). [OpenUrl][34][Abstract/FREE Full Text][35] 7. [↵][36]1. R. C. Whytock et al ., Methods Ecol. Evol. 12, 1080 (2021). [OpenUrl][37] 8. [↵][38]1. B. C. Dhar, 2. N. Y. Lee , Biochip J. 12, 173 (2018). [OpenUrl][39] 9. [↵][40]1. A. P. Hill et al ., Methods Ecol. Evol. 9, 1199 (2018). [OpenUrl][41] 10. [↵][42]1. L. Atzori, 2. A. Iera, 3. G. Morabito , Comput. Netw. 54, 2787 (2010). [OpenUrl][43][CrossRef][44][Web of Science][45] 11. [↵][46]1. W. Shi, 2. J. Cao, 3. Q. Zhang, 4. Y. Li, 5. L. Xu , IEEE Internet Things J. 3, 637 (2016). [OpenUrl][47] 12. [↵][48]1. M. I. Jordan, 2. T. M. Mitchell , Science 349, 255 (2015). [OpenUrl][49][Abstract/FREE Full Text][50] 13. [↵][51]1. R. Aljundi, 2. K. Kelchtermans, 3. T. Tuytelaars , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11254–11263. 14. [↵][52]1. J. R. Sauer, 2. W. A. Link, 3. J. E. Fallon, 4. K. L. Pardieck, 5. D. J. Ziolkowski Jr. , N. Am. Fauna 79, 1 (2013). [OpenUrl][53] 15. [↵][54]1. M. Keller, 2. D. S. Schimel, 3. W. W. Hargrove, 4. F. M. Hoffman , Front. Ecol. Environ. 6, 282 (2008). [OpenUrl][55][CrossRef][56] Acknowledgments: Our perspective on autonomous sensing was developed with the support of the Stengl-Wyer Endowment and the Office of the Vice President for Research Bridging Barriers programs at the University of Texas at Austin, and the National Science Foundation (BCS-2009669). Comments from members of the Keitt laboratory, Planet Texas 2050, A. Wolf, and M. Abelson were invaluable in refining our ideas. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: #ref-15 [16]: #xref-ref-1-1 "View reference 1 in text" [17]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DDiaz%26rft.auinit1%253DS.%26rft.volume%253D366%26rft.issue%253D6471%26rft.spage%253Deaax3100%26rft.epage%253Deaax3100%26rft.atitle%253DPervasive%2Bhuman-driven%2Bdecline%2Bof%2Blife%2Bon%2BEarth%2Bpoints%2Bto%2Bthe%2Bneed%2Bfor%2Btransformative%2Bchange%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aax3100%26rft_id%253Dinfo%253Apmid%252F31831642%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [18]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE3OiIzNjYvNjQ3MS9lYWF4MzEwMCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3My82NTU3Lzg1OC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DAm.%2BNat.%26rft.volume%253D196%26rft.spage%253D1%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.1710231115%26rft_id%253Dinfo%253Apmid%252F29382745%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMDoiMTE1LzcvMTQyNCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3My82NTU3Lzg1OC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DNature%26rft.stitle%253DNature%26rft.aulast%253DCardinale%26rft.auinit1%253DB.%2BJ.%26rft.volume%253D486%26rft.issue%253D7401%26rft.spage%253D59%26rft.epage%253D67%26rft.atitle%253DBiodiversity%2Bloss%2Band%2Bits%2Bimpact%2Bon%2Bhumanity.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature11148%26rft_id%253Dinfo%253Apmid%252F22678280%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: /lookup/external-ref?access_num=10.1038/nature11148&link_type=DOI [27]: /lookup/external-ref?access_num=22678280&link_type=MED&atom=%2Fsci%2F373%2F6557%2F858.atom [28]: /lookup/external-ref?access_num=000304854000027&link_type=ISI [29]: #xref-ref-5-1 "View reference 5 in text" [30]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D521%26rft.spage%253D436%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature14539%26rft_id%253Dinfo%253Apmid%252F26017442%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: /lookup/external-ref?access_num=10.1038/nature14539&link_type=DOI [32]: /lookup/external-ref?access_num=26017442&link_type=MED&atom=%2Fsci%2F373%2F6557%2F858.atom [33]: #xref-ref-6-1 "View reference 6 in text" [34]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.2004702117%26rft_id%253Dinfo%253Apmid%252F32636258%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMjoiMTE3LzI5LzE3MDQ5IjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzczLzY1NTcvODU4LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [36]: #xref-ref-7-1 "View reference 7 in text" [37]: {openurl}?query=rft.jtitle%253DMethods%2BEcol.%2BEvol.%26rft.volume%253D12%26rft.spage%253D1080%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: #xref-ref-8-1 "View reference 8 in text" [39]: {openurl}?query=rft.jtitle%253DBiochip%2BJ.%26rft.volume%253D12%26rft.spage%253D173%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: #xref-ref-9-1 "View reference 9 in text" [41]: {openurl}?query=rft.jtitle%253DMethods%2BEcol.%2BEvol.%26rft.volume%253D9%26rft.spage%253D1199%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [42]: #xref-ref-10-1 "View reference 10 in text" [43]: {openurl}?query=rft.jtitle%253DComput.%2BNetw.%26rft.volume%253D54%26rft.spage%253D2787%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.comnet.2010.05.010%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/external-ref?access_num=10.1016/j.comnet.2010.05.010&link_type=DOI [45]: /lookup/external-ref?access_num=000283039900014&link_type=ISI [46]: #xref-ref-11-1 "View reference 11 in text" [47]: {openurl}?query=rft.jtitle%253DIEEE%2BInternet%2BThings%2BJ.%26rft.volume%253D3%26rft.spage%253D637%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [48]: #xref-ref-12-1 "View reference 12 in text" [49]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DJordan%26rft.auinit1%253DM.%2BI.%26rft.volume%253D349%26rft.issue%253D6245%26rft.spage%253D255%26rft.epage%253D260%26rft.atitle%253DMachine%2Blearning%253A%2BTrends%252C%2Bperspectives%252C%2Band%2Bprospects%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aaa8415%26rft_id%253Dinfo%253Apmid%252F26185243%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [50]: 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{openurl}?query=rft.jtitle%253DFront.%2BEcol.%2BEnviron.%26rft.volume%253D6%26rft.spage%253D282%26rft.atitle%253DFRONT%2BECOL%2BENVIRON%26rft_id%253Dinfo%253Adoi%252F10.1890%252F1540-9295%25282008%25296%255B282%253AACSFTN%255D2.0.CO%253B2%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [56]: /lookup/external-ref?access_num=10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2&link_type=DOI

The world is changing at a rapid pace. Not only are smartphones and technology a way of life, but there are connected IoT devices all around us, enabling everything from smart home appliances to cameras to industrial robots inside of factories. Meanwhile, IoT devices are generating massive amounts of data (in some cases petabytes) that need to be processed and stored. So, why does the edge come into play here instead of the cloud? Edge computing provides the high-powered processing power of the cloud, but closer to the IoT device, enabling a faster turnaround time for analytics and data insights.

Edge AI offers lots of improvement over conventional ML architectures. First of all the latency involved with any network transfer is removed, which can be critical in some use cases. The battery drain involved with streaming data is no longer an issue, allowing for better battery life, and associated costs for data communication are significantly reduced. This is highly beneficial for a number of use cases. Sensors in remote locations like offshore wind farms can come pre-loaded with the algorithms that enable them to make decisions without the complex infrastructure of getting them internet-connected.

With increasing innovations and adoption across industries, the Voice ID biometrics market is forecast to grow by $2.6 billion in the next 4 years according to analyst firm Technavio. About 32% of the market's growth will originate from APAC during the period. The rise in adoption of biometric voice recognition in the healthcare sector is one of the major factors that will have a positive impact on the growth of this market in the coming years. The rising usage of smartphones and personal digital assistants in the healthcare sector has led to apps for electronic prescriptions, diagnosis and treatment, coding and billing. To prevent unauthorized access to confidential data, these apps could be integrated with voice biometrics leading to greater adoption of voice ID biometrics in the coming years.

Manufacturing companies have to fight against the odds of on-going trade issues, supply chain issues in China, decreased demand, etc. The spread of the COVID 19 pandemic has impacted both the supply & demand side and it has brought uncertainty into businesses. With decreased production and halted operations, the industry leaders are looking for alternatives to ensure employee safety and restart operations, after the pandemic abates. From studies, IIoT solution implemented companies are performing better under the current scenario, and leaders strongly back the importance of implementing Industry 4.0 solutions to improve productivity at a reduced cost. Especially leaders that haven't explored the IIoT solutions before would find this as a key enabler at challenging times like this.

Today a number of Artificial Intelligence startups are gaining momentum with significant funding and investments. These startups are not only coming up with great platforms and technologies but mastering the art of innovation. Innovation is at the core of such startups which serve the industry with something that was never explored before. Utilizing hefty amounts of investment from notable investors every now and then, they are marking the commencement of a new era of innovation with several investors coming forward to contribute to the transformative journey of emerging innovators. Here is the list of top 10 artificial intelligence investments/funding that made the headlines in February 2020.

It's no secret - oil and gas industry leaders acknowledge they have been slow to embrace digital technologies. A recent Boston Consulting Group report sums it up well: "The oil and gas industry is not an easy place to go digital." Digitisation brings with it the promise of improved safety and efficiency, yet many companies still have a long way to go. It may be surprising to learn that some businesses even lack the technology to locate their workers ‒ which has clear and potentially dire implications for emergency situations. This is partly because most of the industry is still deeply rooted in 20th-century methodologies, systems and processes.